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743 results for “Auditory”
Accessible Oceans: Auditory Display. 2015 Axial Seamount Eruption
<p>The ten tracks make up an auditory display of the 2015 Axial Seamount Eruption. The ten tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/qRKlQUoRe1n8TWDZOhTn">listen online here</a>.</p> <p>The data comes from the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) and the display is based on the OOI Nugget developed by Dr. Leslie Smith. (<a href="https://datalab.marine.rutgers.edu/ooi-nuggets/axial-eruption/">https://datalab.marine.rutgers.edu/ooi-nuggets/axial-eruption/</a>)</p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></p>
Accessible Oceans: Auditory Display. Net flux of CO2 between Ocean and Atmosphere
<p>The seven tracks make up an auditory display of the net flux of carbon dioxide between the ocean and the atmosphere. The seven tracks in the auditory display are comprised of data sonifications and contextual audio supports (dialogue, auditory icons, and music). You may <a href="https://samply.app/p/RhkRBKbRTueE2F86b5QS">listen online here</a>.</p> <p>The data comes from the National Science Foundation (NSF) Ocean Observatories Initiative (OOI) and the display is based on the OOI Nugget developed by Dr. Leslie Smith. (<a href="https://datalab.marine.rutgers.edu/ooi-nuggets/co2-flux/">https://datalab.marine.rutgers.edu/ooi-nuggets/co2-flux/</a>)</p> <p>The “Accessible Oceans” AISL Pilots and Feasibility study aims to inclusively design auditory displays that support the perception and understanding of ocean data in informal learning environments (ILEs). More can be found on the project website: <a href="https://accessibleoceans.whoi.edu/">https://accessibleoceans.whoi.edu/</a></p>
Comparing effects of auditory and visual disturbances on smallmouth bass parental care behaviors during the summer of 2025 at Douglas Lake, Michigan, USA
A prevalent source of sensory pollution within aquatic systems is recreational motorboats that can impact aquatic organisms through several exposure mechanisms. Auditory and visual sensory disturbances are particularly important as fish may utilize these cues during critical reproductive behaviors such as parental care. Here, we conducted a field study in Douglas Lake, Michigan, and located wild smallouth bass nests actively guarded by males. We exposed smallmouth bass to two sequential treatments of playback auditory noise and visual disturbances. Using an underwater drone, parental care behaviors of smallmouth bass were monitored before, during, and after both auditory and visual disturbances. The results show that auditory and visual disturbances may alter smallmouth bass parental care behaviors differently.
EEG: 3-Stim Auditory Oddball and Rest in Parkinson's
Open the record for dataset details and reuse information.
EEG: Three-Stim Auditory Oddball and Rest in Acute and Chronic TBI
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MEG dataset nonlinguistic auditory statistical learning
<p>MEG data of 24 healthy adults with an auditory nonlinguistic statistical learning paradigm plus data from two subsequent behavioral tasks. For closer description of data see data description file. </p>
Minimal dataset for "Systematic review and meta-analysis of late auditory evoked potentials as a candidate biomarker in the assessment of tinnitus"
<p>This text file contains the minimal dataset necessary to reproduce the results and analyses in the paper: Cardon E et al., "Systematic review and meta-analysis of late auditory evoked potentials as a candidate biomarker in the assessment of tinnitus". Plos One;2020.</p>
Data and materials from: "Towards Auditory Profile-based Hearing-aid Fitting: Fitting Rationale and Pilot Evaluation"
<p>This repository contains materials and data used and described in:</p> <p><strong>Sanchez-Lopez, R., Fereczkowski, M., Santurette, S., Dau, T., Neher, T. (2021). Towards Auditory Profile-based Hearing-aid Fitting: Fitting Rationale and Pilot Evaluation. <em>Audiol. Res.</em> 11, no. 1: 10-21. <a href="https://doi.org/10.3390/audiolres11010002 ">https://doi.org/10.3390/audiolres11010002 </a></strong></p> <p> </p> <p><strong>Abstract</strong></p> <p><strong>Background</strong> - The clinical characterization of hearing deficits for hearing-aid fitting purposes is typically based on the pure-tone audiogram only. In a previous study, a group of hearing-impaired listeners completed a comprehensive test battery designed to tap into different dimensions of hearing abilities. A data-driven analysis of the data yielded four clinically relevant patient subpopulations or “auditory profiles”. The purpose of the current study was to propose and pilot-test profile-based hearing-aid settings to explore their potential for providing more targeted hearing-aid treatment.<br> <strong>Methods </strong>- Four candidate hearing-aid settings were developed and evaluated by a subset of the participants tested previously. The evaluation consisted of multi-comparison preference ratings carried out in realistic sound scenarios.<br> <strong>Results </strong>- Listeners belonging to the different auditory profiles showed different patterns of preference for the tested hearing-aid settings that were largely consistent with the expectations.<br> <strong>Conclusion</strong> - The results of this pilot evaluation support further investigations into stratified, profile-based hearing-aid fitting with wearable hearing aids.</p> <p>Please cite this article when using the data</p> <p> </p> <p><strong>Description of the files:</strong></p> <ul> <li><strong>APBHAF_Audiofiles.zip: </strong>Audio files used in the SenseLabOnline environment. Each folder corresponds to one participant.</li> <li><strong>APBHAF_MUSHA.xlsx: </strong>Raw Data of the MUSHA experiment.</li> <li><strong>APBHAF_MUSHA_Analysis.R: </strong>R code for the data analysis.</li> <li><strong>MUS_SoundScenes.mat</strong>: Mat file with a structure 1x9 MUS with the raw acoustic signals before processing with the hearing-aid simulator. <ul> <li>CurrentMixture: matrix consisting of the simulated acoustic signal recorded by the 4 microphones of the hearing-aid satellites: 1) front-left, 2) back-left, 3) front-right, 4) back-right.</li> <li>CurrentAnchor: matrix consisting of the simulated acoustic signal recorded by the 4 microphones of the hearing-aid satellites: 1) front-left, 2) back-left, 3) front-right, 4) back-right. The Anchor is -6 dB SNR.</li> <li>SampleLabel: Either "Kantine", "Traffic" or "Quiet"</li> <li>ConditionLabel: Either "Cond1", "Cond2" or "Cond3"</li> <li>fsmix: sampling frequency. For all signals must be 32000Hz</li> </ul> </li> <li><strong>MUSHA_Instruction: </strong>Instructions used for explaining the task and the environment.</li> </ul> <p>* The participant IDs in each of the files has been assigned randomly to ensure the anonymization of the data. The pseudo-anonymized data might be shared under request by direct correspondence with the authors.</p>
Effects of Auditory Stimuli During Submaximal Exercise on Cerebral Oxygenation
<p>Manuscript, supplementay files, raw and proprocessed data, code, and materials for the associated registered report.</p>
Human auditory ecology : Extending hearing research to the perception of natural soundscapes by humans in rapidly-changing environments
<p>The audiomaterial corresponding to boreal, tropical and temperate forests, desert, savannah, sub-alpine meadow, and the construction site in New York is copyrighted (license from Wild Sanctuary) and cannot be used without explicit agreement of Bernie Krause. Additional audiomaterial (urban park and street traffic in Paris, France; fast street traffic in Marseille, France; English and French speech material) may only be used with the explicit agreement of the following authors: Jérôme Sueur and Sylvain Haupert (Museum National d'Histoire Naturelle in Paris, France); Sabine Meunier (LMA/CNRS in Marseille, France); Franck Ramus (CNRS in Paris, France) (see Figure legends).</p>
PsPM-SF: SCR, ECG, PPU and respiration measurements from a delay fear conditioning task with auditory CS (monophones/triads), performed during MRI scanning
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG), peripheral pulse unit (PPU) and respiration measurements for 20 healthy unmedicated participants (10 females and 10 males, age range: 19 - 35 years, mean age: 24.2 +/- 4.9) participating in a classical (Pavlovian) discriminant delay fear conditioning experiment with auditory CS, during MRI scanning. Also included are CS and US information, and ratings of CS after the experiment. Simple and complex CS were simple sine tones (4 s), and triads in root position or in first inversion, respectively. US was a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. After the fear conditioning task, participants were first asked to report their subjective estimate of how likely they were to receive a shock after a given CS in the future, on a visual analogue scale of 0-100. Then they were asked to rate pairs of CS sounds with respect to which of the two stimuli they liked less. NOTE: In Staib et al. 2015, this dataset is denoted as SC1F</p>
PsPM-SCB2D: Skin conductance response from a delay fear conditioning task with auditory CS (monophones/triads)
<p>This dataset includes skin conductance response (SCR) for 22 healthy unmedicated participants (15 females and 7 males, age range: 18 - 32 years, mean age: 22.1 +/- 3.4) participating in a classical (Pavlovian) discriminant delay fear conditioning experiment with auditory CS. Also included are CS and US information, and ratings of CS after the experiment. Simple and complex CS were simple sine tones (4 s), and triads in root position or in first inversion, respectively. US was a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. After the fear conditioning task, participants were first asked to report their subjective estimate of how likely they were to receive a shock after a given CS in the future, on a visual analogue scale of 0-100. Then they were asked to rate pairs of CS sounds with respect to which of the two stimuli they liked less.</p>
Auditory Attention Detection Dataset KULeuven
<div>***************************************</div> <p>Please cite the original paper where this data set was presented:</p> <p>Biesmans, W., Das, N., Francart, T., & Bertrand, A. (2016). Auditory-inspired speech envelope extraction methods for improved EEG-based auditory attention detection in a cocktail party scenario. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(5), 402-412.</p> <p>***************************************</p> <p><strong>IMPORTANT MESSAGE FROM THE AUTHORS (January 2024):</strong></p> <p>We have observed that this dataset is widely used in research, establishing it as a standard benchmark for evaluating novel decoding strategies in auditory attention decoding (AAD). We emphasize the critical importance of rigorous cross-validation in such studies. In particular, researchers should be aware of two common and significant validation pitfalls:</p> <ol> <li> <p><strong>Trial fingerprints</strong>: Avoid splitting data from the same experimental trial into training and testing segments. Classifiers can detect whether a test segment belongs to a specific trial if other, non-overlapping segments from that trial are in the training set.</p> </li> <li> <p><strong>Gaze bias</strong>: AAD algorithms that directly classify EEG signals (often referred to as spatial AAD or SpAAD) without explicitly correlating the decoder output signal to the speech stimulus, should <em>not </em>be evaluated on this dataset, as it is affected by gaze-related bias. Instead, use the gaze-controlled dataset of Rotaru et al. available at <a href="https://zenodo.org/records/11058711">https://zenodo.org/records/11058711</a></p> </li> </ol> <p>Further details on both issues are provided below.</p> <p>In the original study by Biesmans et al., which produced this dataset, linear correlation-based methods were employed, and a straightforward random cross-validation sufficed. However, with the recent surge in the application of machine learning techniques, particularly deep neural networks, in tackling the AAD challenge, a more stringent cross-validation approach becomes imperative. Deep networks are susceptible to overfitting to trial-specific patterns in EEG data, even from very brief segments (less than 1 second), leading to the ability to identify the trial source. Since a subject typically maintains attention to the same speaker throughout a trial, having knowledge of the trial effectively results in a perfect attention decoding.</p> <p>We observed that many research papers utilizing our dataset still adhere to the basic random cross-validation method, neglecting the separation of trials into training and testing sets. Consequently, these studies frequently report remarkably high AAD accuracies when using extremely short EEG segments (one or a few seconds). Nevertheless, research has demonstrated that such an approach yields inaccurate and excessively optimistic outcomes. Accuracies often plummet significantly, sometimes even falling below chance levels, when employing a proper cross-validation where this trial bias is removed (e.g., leave-one-trial-out, leave-one-story-out, or leave-one-subject-out cross-validation).</p> <p>This overfitting effect is described in: Corentin Puffay et al., "Relating EEG to continuous speech using deep neural networks: a review", Journal of Neural Engineering 20, 041003, 2023 DOI:10.1088/1741-2552/ace73f</p> <p>Moreover, it's important to note that AAD strategies which directly classify an EEG snippet, rather than explicitly computing a correlation between the decoder output and the corresponding speech envelopes, may be susceptible to an eye-gaze bias. This bias refers to the tendency of the subject to subtly and often unknowingly direct their gaze towards the attended speaker. Given that EEG equipment can inadvertently capture these gaze patterns, it becomes possible to leverage this gaze information, whether intentionally or unintentionally, to enhance AAD performance. It's crucial to highlight that there is a relatively strong eye gaze bias in this dataset (such gaze bias is present in the majority of public AAD datasets).</p> <p>This eye-gaze overfitting effects is discussed in: Rotaru et al. "What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention", Journal of Neural Engineering, vol. 21, 016017, DOI: https://doi.org/10.1088/1741-2552/ad2214. Also available on bioRxiv: https://doi.org/10.1101/2023.07.13.548824</p> <p>To test whether your method is not using gaze as a shortcut, use the Rotaru et al. data set available at <a href="https://zenodo.org/records/11058711">https://zenodo.org/records/11058711</a></p> <p>***************************************</p> <p>Explanation about the data set:</p> <p>This work was done at ExpORL, Dept. Neurosciences, KULeuven and Dept. Electrical Engineering (ESAT), KULeuven.<br>This dataset contains EEG data collected from 16 normal-hearing subjects. EEG recordings were made in a soundproof, electromagnetically shielded room at ExpORL, KULeuven. The BioSemi ActiveTwo system was used to record 64-channel EEG signals at 8196 Hz sample rate. The audio signals, low pass filtered at 4 kHz, were administered to each subject at 60 dBA through a pair of insert phones (Etymotic ER3A). The experiments were conducted using the APEX 3 program developed at ExpORL [1].</p> <p>Four Dutch short stories [2], narrated by different male speakers, were used as stimuli. All silences longer than 500 ms in the audio files were truncated to 500 ms. Each story was divided into two parts of approximately 6 minutes each. During a presentation, the subjects were presented with the six-minutes part of two (out of four) stories played simultaneously. There were two stimulus conditions, i.e., `HRTF' or `dry' (dichotic). An experiment here is defined as a sequence of 4 presentations, 2 for each stimulus condition and ear of stimulation, with questions asked to the subject after each presentation. All subjects sat through three experiments within a single recording session. An example for the design of an experiment is shown in Table 1 in [3]. The first two experiments included four presentations each. During a presentation, the subjects were instructed to listen to the story in one ear, while ignoring the story in the other ear. After each presentation, the subjects were presented with a set of multiple-choice questions about the story they were listening to in order to help them stay motivated to focus on the task. In the next presentation, the subjects were presented with the next part of the two stories. This time they were instructed to attend to their other ear. In this manner, one experiment involved four presentations in which the subjects listened to a total of two stories, switching attended ear between presentations. The second experiment had the same design but with two other stories. Note that the Table was different for each subject or recording session, i.e., each of the elements in the table were permuted between different recording sessions to ensure that the different conditions (stimulus condition and the attended ear) were equally distributed over the four presentations. Finally, the third experiment included a set of presentations where the first two minutes of the story parts from the first experiment, i.e., a total of four shorter presentations, were repeated three times, to build a set of recordings of repetitions. Thus, a total of approximately 72 minutes of EEG was recorded per subject. </p> <p>We refer to EEG recorded from each presentation as a trial. For each subject, we recorded 20 trials - 4 from the first experiment, 4 from the second experiment, and 12 from the third experiment (first 2 minutes of the 4 presentations from experiment 1 X 3 repetitions). The EEG data is stored in subject specific mat files of the format 'Sx', 'x' referring to the subject number. The audio data is stored as wav files in the folder 'stimuli'. Please note that the stories were not of equal lengths, and the subjects were allowed to finish listening to a story, even in cases where the competing story was over. Therefore, for each trial, we suggest referring to the length of the EEG recordings to truncate the ends of the corresponding audio data. This will ensure that the processed data (EEG and audio) contains only competing talker scenarios. Each trial was high-pass filtered (0.5 Hz cut off) and downsampled from the recorded sampling rate of 8192 Hz to 128 Hz. Artifacts were removed using the MWF-filtering method in [4]. Please get in touch with the team (of Prof. Alexander Bertrand or Prof. Tom Francart) if you wish to obtain the raw EEG data (without the mentioned high-pass filtering and artifact removal).</p> <p>Each trial (trial*.mat) contains the following information: </p> <p><strong>RawData.Channels</strong> : channel numbers (1 to 64)<br><strong>RawData.EegData</strong> : EEG data (samples X channels)<br><strong>FileHeader.SampleRate</strong> : Sampling frequency of the saved data<br><strong>TrialID</strong> : a number between 1 to 20, showing the trial number<br><strong>attended_ear</strong> : the direction of attention of the subject. 'L' for left, 'R' for right<br><strong>stimuli</strong> : cell array with stimuli{1} and stimuli{2} indicating the name of audio files presented in the left ear and the right ear of the subject respectively<br><strong>condition</strong> : stimulus presentation condition. 'HRTF' - stimuli were filtered with HRTF functions to simulate audio from 90 degrees to the left and 90 degrees to the right of the speaker, 'dry' - a dichotic presentation in which there was one story track each presented separately via the left and the right earphones.<br><strong>experiment</strong> : the number of the experiment (1,2 or 3)<br><strong>part</strong> : part of the story track being presented (can be 1 to 4 for experiments 1 and 2, and 1 to 12 for experiment 3)<br><strong>attended_track</strong> : the attended story track. '1' for track 1 and '2' for track 2. Each track maintains continuity of the story. In Experiment 1, attention is always to track 1, and in Experiment 2, attention is always to track 2. <br><strong>repetition</strong> : binary variable indicating where the trial is a repetition (of presented stimuli) or not.<br><strong>subject</strong> : subject id of the format 'Sx', 'x' being the subject number.</p> <p>The 'stimuli' folder contains wav files of the format: part{part number}_track{track number}_{condition}.wav. Although the folder contains stimuli with HRTF filtering as well, for the analysis, we have assumed knowledge of the original clean stimuli (i.e. stimuli presented under the 'dry' condition), and hence envelopes were extracted only from part{part number}_track{tracknumber}_dry.wav files.</p> <p>The Matlab file 'preprocess_data.m' gives an example of how the synchronization and preprocessing of EEG and audio data can be done as described in [5]. Dependency: AMToolbox.</p> <p>This dataset has been used in [3, 5-14] (not updated anymore). </p> <p>[1] Francart, T., Van Wieringen, A., & Wouters, J. (2008). APEX 3: a multi-purpose test platform for auditory psychophysical experiments. <em>Journal of neuroscience methods</em>, 172(2), 283-293.<br>[2] Radioboeken voor kinderen, <a href="http://radioboeken.eu/kinderradioboeken.php?lang=NL">http://radioboeken.eu/kinderradioboeken.php?lang=NL</a>, 2007 (Accessed: 30 March 2015)<br>[3] Das, N., Biesmans, W., Bertrand, A., & Francart, T. (2016). The effect of head-related filtering and ear-specific decoding bias on auditory attention detection.<em> Journal of neural engineering</em>, 13(5), 056014.<br>[4] Somers, B., Francart, T., & Bertrand, A. (2018). A generic EEG artifact removal algorithm based on the multi-channel Wiener filter. <em>Journal of neural engineering</em>, 15(3), 036007.<br>[5] Das, N., Vanthornhout, J., Francart, T., & Bertrand, A. (2019). Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research. <em>bioRxiv</em> 541318; doi: <a href="https://doi.org/10.1101/541318">https://doi.org/10.1101/541318</a><br>[6] Biesmans, W., Das, N., Francart, T., & Bertrand, A. (2016). Auditory-inspired speech envelope extraction methods for improved EEG-based auditory attention detection in a cocktail party scenario. <em>IEEE Transactions on Neural Systems and Rehabilitation Engineering</em>, 25(5), 402-412.<br>[7] Das, N., Van Eyndhoven, S., Francart, T., & Bertrand, A. (2016, August). Adaptive attention-driven speech enhancement for EEG-informed hearing prostheses. In 2016 <em>38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)</em> (pp. 77-80). IEEE.<br>[8] Van Eyndhoven, S., Francart, T., & Bertrand, A. (2016). EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses. <em>IEEE Transactions on Biomedical Engineering</em>, 64(5), 1045-1056.<br>[9] Das, N., Van Eyndhoven, S., Francart, T., & Bertrand, A. (2017, August). EEG-based attention-driven speech enhancement for noisy speech mixtures using N-fold multi-channel Wiener filters. In 2017 <em>25th European Signal Processing Conference (EUSIPCO)</em> (pp. 1660-1664). IEEE.<br>[10] Narayanan, A. M., & Bertrand, A. (2018, July). The effect of miniaturization and galvanic separation of EEG sensor devices in an auditory attention detection task. In 2018 <em>40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)</em> (pp. 77-80). IEEE.<br>[11] Deckers, L., Das, N., Ansari, A. H., Bertrand, A., & Francart, T. (2018). EEG-based detection of the attended speaker and the locus of auditory attention with convolutional neural networks. <em>bioRxiv </em>475673; doi: <a href="https://doi.org/10.1101/475673">https://doi.org/10.1101/475673</a><br>[12] Narayanan, A. M., & Bertrand, A. (2019). Analysis of miniaturization effects and channel selection strategies for EEG sensor networks with application to auditory attention detection. <em>IEEE Transactions on Biomedical Engineering</em>.<br>[13] Geirnaert, S., Francart, T., & Bertrand, A. A New Metric to evaluate auditory attention detection performance based on a Markov chain. Accepted for publication in <em>Proc. European Signal Processing Conference (EUSIPCO)</em>, A Coruna, Spain, Sep. 2019.<br>[14] Geirnaert, S., Francart,T., Bertrand A. (2019). An Interpretable performance metric for auditory attention decoding algorithms in a context of neuro-steered gain control. <em>bioRxiv </em>745695; doi: <a href="https://doi.org/10.1101/745695">https://doi.org/10.1101/745695</a> </p>
Dataset: Auditory brainstem responses to varying stimulus presentation rates of 12 bat species in the wild and captivity
<p>Dataset for the Dataset Publication: Auditory brainstem responses to varying stimulus presentation rates of 12 bat species in the wild and captivity</p> <p>There are two datasets available: 1) the measured ABRs from Experiments 1 and 2 and 2) the extracted IOIs:</p> <ol> <li>ABR measurements:</li> </ol> <p>The filename of the ABR recordings from Experiment 1 include the species name, individual ID, sex, stimulus presentation rate (indicated as “modrate”) and recording day and time (yyyyddmm). Each recording file contains 256 measurements of the same stimulus and stimulus presentation rate in columns. An exemplary filename would be “Carollia_perspicillata_cp6male_modrate6_20190905T125903”, meaning that this is a recording of <em>Carollia perspicillata</em> individual cp6 of sex male, tested with a stimulus presentation rate of 6 Hz on the 09.05.2019, and the file was saved at 12:59:03 (the T between date and time stands for “Time”).</p> <p>The filename of the ABR recordings from Experiment 2 include the place of the Experiments (Bad Segeberg) and species name (<em>C. perspicillata</em>), individual ID, sex, stimulus presentation rate (indicated as “modrate”) and recording day and time (yyyymmdd; be aware, that the date format is different between Experiment 1 and 2). Each recording file contains 256 measurements of the same stimulus and stimulus presentation rate in columns. An exemplary filename would be “BadSegeberg_cper_1_male_modrate6_20200622T140952_stimulus_ST01_short ”, meaning that this is a recording of <em>Carollia perspicillata</em> individual cp6 of sex male, tested with a stimulus presentation rate of 6 Hz on the 09.05.2019, and the file was saved at 12:59:03 (the T between date and time stands for “Time”), the individual was presented with stimulus example 01 of the short stimuli.</p> <ol> <li>IOI recordings</li> </ol> <p>The recordings of Inter-Onset-Intervals are all in one single csv file and species and sequence ID is given per row, to be able to analyze the data further.</p> <p> </p>
Visual and Auditory vection stimuli reduce motion sickness
<p>This is the raw data and the full data set from all of our participants included in the analysis for this experiment. </p> <p>The raw data represents the data recorded throughout the experience. Motion Sickness scores, performance on reading task, performance on attention task. </p> <p>While the full data set (Final1) additionally includes questionnaire data (SSQ, NASA TLX, IPQ,...) as well as demographic data of the participants. </p>
Auditory Selective Attention Switch in a Virtual Reality Classroom Environment
<p><strong>General</strong></p> <p>The audio-visual Auditory Selective Attention VR Proof of Concept (asaVRpoc) project serves to investigate the auditory selective attention switch in a close-to-real-life classroom setting. This dataset consists of a Unity project and Matlab code used to collect data on the voluntary switching of auditory selective attention in a virtual reality classroom scenario.</p> <p>The dataset contains:</p> <ul> <li>Unity project for visual display and the experiment structure</li> <li>Matlab code for experiment preparation and HpFT measurement</li> <li>Data collected in the experiment (experiment performance, head tracking, questionnaires)</li> </ul> <p><strong>Experiment preparation using Matlab</strong></p> <p>The code and software used to prepare the experiment is provided in the folder<em> "matlab_asaVRpoc"</em>.</p> <p>The Matlab code used to prepare the trials for each participant as well as to measure the HpTFs. For the HpTF measurements, the ITA Toolbox for Matlab was used and is provided (https://git.rwth-aachen.de/ita/toolbox commit hash: 598675ef704c178365f53d41e03ff4b11dea390f). A developmental version of Virtual acoustics (VA) 2020b (https://www.virtualacoustics.org/VA/overview/) is provided.</p> <p>Software requirements:</p> <ul> <li>Matlab 2019a or higher</li> <li>ITA Toolbox for Matlab installed</li> </ul> <p> </p> <p><strong>Experiment conduction in Unity</strong></p> <p>The Unity project is provided in the folder<em> "unity_pc_asaVRpoc"</em>.</p> <p>Therefore, a virtual classroom with some basic furniture is provided. The used models, prefabs and plugins can be found in the Assets folder.</p> <p>Note that the <em>acoustic stimuli are NOT provided</em> with this Unity project. The stimuli are available on request from the Institute for Hearing Technology and Acoustics, RWTH Aachen University.</p> <p>This Unity project was intended for the use in virtual reality using an HMD and respective controllers for input. However, it can also be used on a desktop pc. The mode can be changed using the "VRMode" toggle as described below.<br> The audio reproduction is realized using the Unity plugin for Virtual Acoustics (VA, http://www.virtualacoustics.org/).</p> <p>Software requirements:</p> <ul> <li>Unity 2019.4.21.f1.</li> <li>SteamVR 1.19.7</li> <li>Virtual Acoustics v2021a, VAUnity: https://git.rwth-aachen.de/ita/VAUnity</li> </ul> <p> </p> <p><strong>Data evaluation</strong></p> <p>The collected data is provided in the folder<em> "dataEvaluation_asaVRpoc"</em>. This folder contains three types of data: the raw data collected in the experiment (reaction times and error rates), the head tracking data and responses from the simulator sickness questionnaire (before and after the experiment) and the igroup presence questionnaire (after the experiment). Matlab code for the evaluation of the head tracking data and the questionnaires is provided.</p> <p> </p>
Audiovisual, Gaze-controlled Auditory Attention Decoding Dataset KU Leuven (AV-GC-AAD)
<p>This dataset is described in detail in the following journal paper [1]:<br>Rotaru, I., Geirnaert, S., Heintz, N., Van de Ryck, I., Bertrand, A., & Francart, T. (2024). What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention. Journal of Neural Engineering, 21(1), 016017.<br><a href="https://iopscience.iop.org/article/10.1088/1741-2552/ad2214/meta">https://iopscience.iop.org/article/10.1088/1741-2552/ad2214/meta</a></p> <p><em><strong> If using this dataset, please cite the original paper above and the current Zenodo repository. </strong></em></p> <p><strong>Note from the authors: </strong>Recent evaluations reveal that various published AAD (Auditory Attention Decoding) algorithms do not achieve significant above-chance performance on this AV-GC-AAD dataset, and in particular on the two gaze-incongruent conditions 'MovingVideo' and 'MovingTargetNoise'). This suggests that previously reported successes may have been largely influenced by eye gaze confounds present in other datasets, which can be exploited as shortcuts by machine learning algorithms. Despite these findings, poor performance on the AV-GC-AAD dataset is often dismissed, with reasons cited such as insufficient training data, high heterogeneity in audiovisual conditions, or the claim that participants were unable to focus their auditory attention due to the complexity of the instructions.</p> <p>To address these concerns, we provide a supplementary technical report (and accompanying code), showcasing results from a simple linear stimulus reconstruction AAD algorithm applied to this dataset. Our findings demonstrate that high AAD accuracy can be achieved within individual conditions, and that the model generalizes across conditions, new subjects, and even across different datasets.</p> <p><a title="https://doi.org/10.48550/arxiv.2412.01401" href="https://doi.org/10.48550/arXiv.2412.01401" target="_blank" rel="noreferrer noopener">Report</a> | <a title="https://github.com/alexanderbertrandlab/linear-stimulus-reconstruction-aad-av-gc-aad-dataset" href="https://github.com/AlexanderBertrandLab/linear-stimulus-reconstruction-AAD-AV-GC-AAD-dataset" target="_blank" rel="noreferrer noopener">Matlab Code</a></p> <p>Through this report, we aim to remove any doubts that the AV-GC-AAD dataset's limitations are the primary cause of AAD algorithms failing to exceed chance-level performance. Additionally, this report and its accompanying code offer a simple baseline evaluation procedure, which can serve as a minimal benchmark for testing more advanced AAD algorithms on this dataset.</p> <p><em>When reporting results on this data set, it is good practice to show performance for each condition separately, since 2 of the 4 conditions still contain gaze shortcuts, which could be exploited by machine learning algorithms. </em></p> <p>________________________________________________________________________________</p> <p><strong>Dataset description</strong></p> <p>This work was performed at ExpORL, Dept. Neurosciences, KU Leuven and Dept. Electrical Engineering (ESAT), KU Leuven (Belgium), with the goal of investigating and controlling for the effect of gaze during a competing listening task.</p> <p>The full dataset contains EEG and EOG data collected from 16 normal-hearing subjects, during a competing listening task, where the subjects were instructed to focus on one of two competing speech signals. However, subjects 2, 5 and 6 were excluded from the online repository due to not consenting to sharing their data in a public database (cf. signed informed consents approved by KU Leuven Ethical Committee). EEG recordings were conducted in a soundproof, electromagnetically shielded room at ExpORL, KU Leuven. The BioSemi ActiveTwo system was used to record 64-channel EEG signals at 8196 Hz sample rate. Additionally, the participants' gaze movements were measured via 4 EOG (electrooculography) electrodes placed symmetrically around the eyes. </p> <p>The audio signals were administered to each subject at 65 dB SPL through a pair of insert phones (Etymotic ER10). In some experimental trials, the video depicting the attended talker was also presented on the screen. The original presented speech and video stimuli (.wav and .mp4 files) are excluded from the dataset due to copyrights. However, the acoustic envelopes of the attended and unattended audio stimuli are calculated and included in the dataset (see below). <br>The experiments were conducted using custom-made Python scripts.</p> <p>The experimental trials were split into 2 blocks. Each block consisted of the following sequence of conditions: MovingVideo, MovingTargetNoise, NoVisuals, StaticVideo. The auditory task was the same for all conditions: the subjects had to attend to one of the two presented talkers, as indicated by an arrow on the screen. The visual task differed across conditions:</p> <ul> <li>MovingVideo: the subjects had to follow the moving video of the to-be-attended speaker presented on a randomized horizontal trajectory on the screen.</li> <li>MovingTargetNoise: the subjects had to follow a moving cross-hair presented on a randomized horizontal trajectory on the screen.</li> <li>NoVisuals: a black screen was presented and the subjects had to fixate on an imaginary point in the center of the screen while minimizing the eye movements.</li> <li>StaticVideo: the subjects had to fixate the static video of the to-be-attended speaker presented on the same side with the audio stimulus of the attended speaker.</li> </ul> <p>The full description of all experimental conditions can be consulted in [1].</p> <p>Each trial/condition lasted for 10 minutes, with a <strong><em>spatial switch</em></strong> in attention after 5 minutes (i.e., the presented speech stimuli were programmed to swap sides - from L to R or vice versa, such that after the switch the subjects kept listening to the same speaker, but coming from the opposite spatial location). This means that the participant kept attending to the same speaker throughout an entire trial. To keep the subjects motivated, they had to answer one comprehension question related to the attended acoustic stimulus after each trial.</p> <p>For each subject, there is a<strong> .mat file</strong> containing the following variables:<br><strong>conditionID:</strong> the condition ID for each trial <br><strong>data</strong>: the preprocessed EEG and EOG data for each trial (first 64 channels are EEG, last 4 are EOG)<br><strong>fs:</strong> the sampling rate of the EEG, EOG and stimuli envelopes<br><strong>initAttention</strong>: the initial spatial location of the attended stimulus for each trial<br><strong>metadata</strong>: the original metadata (e.g. channel names, triggers) saved in the raw .bdf files for each trial<br><strong>params</strong>: the filtering parameters used for each trial<br><strong>randomization</strong>: the randomization parameters (e.g. presented stimuli, attention switch times etc.) for each trial<br><strong>stimulus</strong>: the precalculated envelopes for the attended and unattended stimuli for each trial<br><strong>subjID</strong>: the anonymised ID of the current subject</p> <p><strong>Preprocessing EEG and EOG</strong></p> <p>All the following preprocessing steps were applied per trial. The EEG was initially downsampled using an antialiasing filter from 8192 Hz to 256 Hz. The data was then filtered between 1–40 Hz using a zero-phase Chebyshev filter (type II, with 80 dB attenuation at 10% outside the passband). Finally, downsampling to 128 Hz was performed to speed up computation.</p> <p><strong>Speech envelopes extraction</strong></p> <p>The original speech signals at 44100 Hz were downsampled to 8192 Hz (to match the EEG sampling rate). They were then passed through a gammatone filterbank, which roughly approximates the spectral decomposition as performed by the human auditory system. Per subband, the audio envelopes were extracted, and their dynamic range was compressed using a power-law operation with exponent 0.6 (as proposed in [2]). Each subband was then bandpass-filtered with the same filter used for the EEG data. The resulting subband envelopes were then summed to construct a single broadband envelope. Finally, the envelope signals were downsampled to 128 Hz to match the sampling rate of the preprocessed EEG.</p> <p><strong>Notes</strong></p> <ol> <li>For subjects 1-3, 6 trials corresponding to 3 conditions (MovingVideo, NoVisuals, StaticVideo) were measured.</li> <li>For subjects 4-16, 8 trials corresponding to 4 conditions (MovingVideo, MovingTargetNoise, NoVisuals, StaticVideo) were measured.</li> <li>For subject 14, trial 2 from the StaticVideo condition was not recorded due to some technical problems.</li> <li>In the dataset, 'FixedVideo' is the alias name for the 'StaticVideo' condition described in [1].</li> <li>The EEG/EOG data was not referenced. Before further analysis, rereferencing the data (e.g., to an arbitrary EEG channel, or the common-average of all channels) is necessary to achieve a better common-mode rejection and thus increase the SNR of recorded data. (for details, see https://www.biosemi.com/faq/cms&drl.htm)</li> </ol> <p><strong>References</strong></p> <p>[1] Rotaru, Iustina, et al. "What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention." <em>Journal of Neural Engineering</em> 21.1 (2024): 016017.</p> <p>[2] Biesmans, Wouter, et al. "Auditory-inspired speech envelope extraction methods for improved EEG-based auditory attention detection in a cocktail party scenario." <em>IEEE Transactions on neural systems and rehabilitation engineering</em> 25.5 (2016): 402-412.</p>
EEG and audio dataset for auditory attention decoding
<p>This dataset contains EEG recordings from 18 subjects listening to one of two competing speech audio streams. Continuous speech in trials of ~50 sec. was presented to normal hearing listeners in simulated rooms with different degrees of reverberation. Subjects were asked to attend one of two spatially separated speakers (one male, one female) and ignore the other. Repeated trials with presentation of a single talker were also recorded. The data were recorded in a double-walled soundproof booth at the Technical University of Denmark (DTU) using a 64-channel Biosemi system and digitized at a sampling rate of 512 Hz. Full details can be found in:</p> <ul> <li><strong>Søren A. Fuglsang, Torsten Dau & Jens Hjortkjær (2017): Noise-robust cortical tracking of attended speech in real-life environments. <em>NeuroImage</em>, 156, 435-444</strong></li> </ul> <p>and</p> <ul> <li><strong>Daniel D.E. Wong, Søren A. Fuglsang, Jens Hjortkjær, Enea Ceolini, Malcolm Slaney & Alain de Cheveigné: A Comparison of Temporal Response Function Estimation Methods for Auditory Attention Decoding. Frontiers in Neuroscience, </strong><a href="https://doi.org/10.3389/fnins.2018.00531">https://doi.org/10.3389/fnins.2018.00531</a></li> </ul> <p>The data is organized in format of the publicly available <a href="https://zenodo.org/record/1198430">COCOHA Matlab Toolbox</a>. The preproc_script.m demonstrates how to import and align the EEG and audio data. The script also demonstrates some EEG preprocessing steps as used the Wong et al. paper above. The AUDIO.zip contains wav-files with the speech audio used in the experiment. The EEG.zip contains MAT-files with the EEG/EOG data for each subject. The EEG/EOG data are found in <strong>data.eeg</strong> with the following channels:</p> <ul> <li>channels 1-64: scalp EEG electrodes</li> <li>channel 65: right mastoid electrode</li> <li>channel 66: left mastoid electrode</li> <li>channel 67: vertical EOG below right eye</li> <li>channel 68: horizontal EOG right eye</li> <li>channel 69: vertical EOG above right eye</li> <li>channel 70: vertical EOG below left eye</li> <li>channel 71: horizontal EOG left eye</li> <li>channel 72: vertical EOG above left eye</li> </ul> <p>The <strong>expinfo</strong> table contains information about experimental conditions, including what what speaker the listener was attending to in different trials. The expinfo table contains the following information:</p> <ul> <li>attend_mf: attended speaker (1=male, 2=female)</li> <li>attend_lr: spatial position of the attended speaker (1=left, 2=right)</li> <li>acoustic_condition: type of acoustic room (1= anechoic, 2= mild reverberation, 3= high reverberation, see Fuglsang et al. for details)</li> <li>n_speakers: number of speakers presented (1 or 2)</li> <li>wavfile_male: name of presented audio wav-file for the male speaker</li> <li>wavfile_female: name of presented audio wav-file for the female speaker (if any)</li> <li>trigger: trigger event value for each trial also found in data.event.eeg.value</li> </ul> <p>DATA_preproc.zip contains the preprocessed EEG and audio data as output from preproc_script.m.</p> <p>The dataset was created within the <a href="https://cocoha.org/">COCOHA</a><a href="https://cocoha.org/"> Project</a>: Cognitive Control of a Hearing Aid</p>
Raw data for "Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task"
<p>Raw data for the simulation study " Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task" [1].</p> <p>[1] Josupeit, A., Schoenmaker, E., van de Par, S., & Hohmann, V. (2018). Sparse periodicity‐based auditory features explain human performance in a spatial multitalker auditory scene analysis task. <em>European Journal of Neuroscience</em>, https://doi.org/10.1111/ejn.13981.</p>
Investigating the effect of cochlear synaptopathy on envelope following responses using a model of the auditory nerve
<p>Dataset containing the recorded and simulated data reported in the manuscript "Investigating the effect of cochlear synaptopathy on envelope following responses using a model of the auditory nerve" published in the Journal of the Association for Research in Otolaryngology, JARO (<a href="https://doi.org/10.1007/s10162-019-00721-7">https://doi.org/10.1007/s10162-019-00721-7</a>):</p> <ol> <li>RECORDED Envelope Following Responses (EFR) in normal-hearing (NH) threshold and hearing-impaired (HI) human listeners using deeply (m = 85%) and shallowly (m = 25%) modulated sinusoidally amplitude modulated (SAM) tones.</li> <li>SIMULATED EFRs using the auditory nerve (AN) model by Zilany et al. (2009, 2014).</li> </ol> <p>Files content and structure:</p> <p><strong>Recorded EFRs</strong></p> <p><strong>Fig. 2:</strong></p> <ul> <li><em>fig2__recorded_efr.csv</em>: <br> EFR recordings as a function of stimulus level (EFR magnitude-level functions) for the NH and HI listeners using two modulation depths.</li> </ul> <p>The file containing the recorded EFR data have the following columns:</p> <ul> <li><em>lvl</em>: Stimulation level</li> <li><em>m85_ok: </em>EFR magnitude (dB re to 1 µV) using m = 85%. Significant responses (F-test = 1)</li> <li><em>m85_ko: </em>EFR magnitude (dB re to 1 µV) using m = 85%. Non-significant responses (F-test = 0)</li> <li><em>m85_bkg: </em>Estimated background noise magnitude (dB re to 1 µV) for the recordings when m = 85%</li> <li><em>m25_ok: </em>EFR magnitude (dB re to 1 µV) using m = 25%. Significant responses (F-test = 1)</li> <li><em>m25_ko: </em>EFR magnitude (dB re to 1 µV) using m = 25%. Non-significant responses (F-test = 0)</li> <li><em>m25_bkg: </em>Estimated background noise magnitude (dB re to 1 µV) for the recordings when m = 25% </li> <li><em>subj: </em>Listener id</li> <li>hearing: Hearing group (nh | hi) of the listener</li> </ul> <p><strong>Simulated EFRs:</strong></p> <p><strong><em>Files with the simulation results summing across frequency and SR fiber type</em></strong></p> <p><strong>Fig. 4:</strong></p> <ul> <li><em>fig4a__simul_efr__nh_23ohc_13ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming 2/3 of OHC loss and 1/3 of IHC loss (Fig. 4a).</li> <li><em>fig4b__simul_efr__nh_slp_thres_all_ohc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming sloping threshold at extended high frequencies (EHF) and all of OHC loss (Fig. 4b).</li> <li><em>fig4c__simul_efr__nh_slp_thres_all_ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming sloping threshold at extended high frequencies (EHF) and all of IHC loss (Fig. 4c).</li> <li><em>fig4d__simul_efr__hi_23ohc_13ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI listeners (average) assuming 2/3 of OHC loss and 1/3 of IHC loss (Fig. 4d).</li> <li><em>fig4e__simul_efr__hi_all_ohc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming all of OHC loss (Fig. 4e).</li> <li><em>fig4f__simul_efr__hi_all_ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming all of IHC loss (Fig. 4f).</li> <li><em>fig4g__simul_efr__hi_slp_thres_23ohc_13ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI listeners (average) assuming sloping threshold at EHF and 2/3 of OHC loss and 1/3 of IHC loss (Fig. 4d).</li> <li><em>fig4h__simul_efr__hi_slp_thres_all_ohc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming sloping threshold at EHF and all of OHC loss (Fig. 4e).</li> <li><em>fig4i__simul_efr__hi_slp_thres_all_ihc__no_cs.csv</em>: <br> Simulated EFR magnitude-level function for the HI threshold listeners (average) assuming sloping threshold at EHF and and all of IHC loss (Fig. 4f).</li> </ul> <p> </p> <p><strong>Fig. 5:</strong></p> <ul> <li><em>fig5a__simul_efr__nh__cs_ms_ls_100p.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners (average) assuming cochlear synaptopathy (CS) of a 100% of loss of only medium- and low-spontaneous rate (SR) AN fibers (Fig. 5a).</li> <li><em>fig5b__simul_efr__nh09_cs__approx</em>.<em>csv</em>: <br> Simulated EFR magnitude-level function to approximate the data for the NH threshold listener NH09 including CS (Fig. 5b).</li> <li><em>fig5c__simul_efr__hi04_cs__approx</em>.<em>csv</em>: <br> Simulated EFR magnitude-level function to approximate the data for the HI listener HI04 including CS (Fig. 5c).</li> </ul> <p> </p> <p><strong>Fig. 6:</strong></p> <p>Files with the simulation results for the NH threshold listener in different characteristic frequency (CF) bands and SR fiber types</p> <ul> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m12.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 12%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m25.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 25%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m50.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 50%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m85.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 85%.</li> <li><em>fig6__simul_efr__nh_cf_band_analys__no_cs__m100.mat</em>: <br> Simulated EFR magnitude-level function for the NH using a SAM tone with m = 100%.</li> </ul> <p> </p> <p><strong>Fig. 7:</strong></p> <ul> <li><em>fig7a__simul_efr__bw_analys__32oct_[20, 40, 60, 80, 100]p.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners assuming CS of a bandwidth (BW) of 3/2-octave for a loss of AN fibers ranging from 20% to 100% (Fig. 7a).</li> <li><em>fig7b__simul_efr__bw_analys__1oct_[20, 40, 60, 80, 100]p.csv</em>: <br> Simulated EFR magnitude-level function for the NH threshold listeners assuming CS of a bandwidth (BW) of 1-octave for a loss of AN fibers ranging from 20% to 100% (Fig. 7b).</li> <li><em>fig7c__simul_efr__bw_analys__13oct_[20, 40, 60, 80, 100]p.csv</em>:<br> Simulated EFR magnitude-level function for the NH threshold listeners assuming CS of a bandwidth (BW) of 1/3-octave for a loss of AN fibers ranging from 20% to 100% (Fig. 7c).</li> </ul> <p> </p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p>The structure of the .csv files that contain the EFR simulations is:</p> <ul> <li><em>lvl</em>: Stimulation level</li> <li><em>mgn_mxxx: </em>Simulated EFR magnitude (a.u. in dB) for each modulation depth (100%, 85%, 50%, 25% and 12%)</li> <li><em>bkg_mxxx: </em>Estimate of the background noise floor (a.u. in dB) for each modulation depth. </li> <li><em>ftest_mxxx: </em>Result of the F-test statistical test (0 or 1) for each modulation depth.</li> </ul> <p> </p> <p>The structure of the .mat files that contain the EFR simulations is:</p> <ul> <li><em>exper_type</em>: Name of the simulated experiment</li> <li><em>species</em>: Species used in the AN model (in this study is always 2: human)</li> <li><em>species_age</em>: (Not used in this work). Age of the animal when species is 4: mouse</li> <li>modulation<em>: </em>Modulation depth of the SAM tone used in the simulation <em>(100%, 85%, 50%, 25% or 12%)</em></li> <li><em>f_on: </em>Center frequency of the on-frequency band (<em>2000 Hz</em>)</li> <li><em>f_off_hf: </em>Center frequency of the first off-frequency band (<em>3000 Hz</em>)</li> <li><em>f_off_vhf: </em>Center frequency of the second off-frequency band (<em>7000 Hz</em>) </li> <li><em>f_off_uvhf: </em>Center frequency of the third off-frequency band (<em>12000 Hz</em>)</li> <li><em>lvl_vect: </em>Stimulus level vector <em>(from 5 to 100 dB SPL, in steps of 5 dB)</em></li> <li><em>simul_efr </em> Structure with the simulated data <ul> <li>The data structure contains many fields which are matricies of size 3x20. The columns are the 20 stimulus levels defined in <em>lvl_vect</em>, and the first row is the simulated EFR, the second row is the estimates background noise floor in the simulation, and the third row is the output of the F-test statistics.</li> <li>The structure fields can be divided in 4 groups: <ul> <li><em>ihc_</em> Responses from the IHC (not shown in the paper)</li> <li><em>an_hs_</em> Responses from the High-SR fibers in the AN</li> <li><em>an_ms_</em> Responses from the Medium-SR fibers in the AN</li> <li><em>an_ls_</em> Responses from the Low-SR fibers in the AN</li> </ul> </li> <li>Each group has 5 responses corresponding to the on-frequency band (<em>_on</em>) and the three off-frequency bands (<em>_off_hf</em>, <em>_off_vhf</em>, <em>_off_uvhf</em>); and the sum across frequencies (<em>_across_f</em>)</li> </ul> </li> </ul>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.