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2 results for “delta band”
EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise'.
<p>The repository contains the unprocessed EEG data recorded for the publications [1, 2]. For convenience, the onsets of the EEG data provided here are time-aligned with the onsets of the audio books in the 'audiobooks' folder, and the EEG data are provided in HDF5 format. Please refer to the original version of this dataset for more details.</p> <p>More details, as well as the original data files, are available at the original repository <a href="https://doi.org/10.5281/zenodo.7086209">here</a>.</p> <p>Examples of using these data (preprocessing, fitting linear models) can be found <a href="https://github.com/Mike-boop/trf-examples">here</a>.</p> <p>The English conditions (clean, lb, mb, hb, fM, fW) comprised a single recording session. The Dutch conditions (cleanDutch, lbDutch, mbDutch, hbDutch) comprised a separate recording session. You see which participants took part in each session in session_info.json.</p> <p>Please note some details about the stimulus presentation for the various listening conditions:</p> <ul> <li>English speech-in-babble-noise (lb, mb, hb): babble noise was played by itself for one second before the audiobook track began. The babble noise was also played for one second after the audiobook track ended. Therefore, you should discard the first second and the last second from these trial during your analysis.</li> <li>Dutch speech-in-babble-noise (lbDutch, mbDutch, hbDutch): the story (narrated in Dutch) was played by itself for one second before the babble noise track began. Then, the babble noise was increased linearly in amplitude for one second. Therefore, you should discard the first two seconds from these trials during your analysis.</li> <li>Dutch in quiet, and Dutch-in-babble-noise (cleanDutch, lbDutch, mbDutch, hbDutch): some English sentences were embedded in the Dutch narratives in order to encourage attention. You should crop these from your analysis. The onsets and offsets of the English sentences (in samples, at 44100Hz) are provided in the audiobooks/*Dutch/english_onsets_info.json files.</li> <li>Competing-speakers conditions (fM, fW): sometimes the attended track is longer than the unattended track, or vice-versa. The onsets of both tracks are aligned. You should crop the trial to the length of the shortest track for your analysis.</li> </ul> <p>If you use this data, please cite the original publications, as well as this repository [1,2,3].</p> <p>[1] Etard O, Kegler M, Braiman C, Forte A E and Reichenbach T. “Decoding of selective attention to continuous speech from the human auditory brainstem response” 2019. <em>NeuroImage</em> <strong>200</strong> 1–11</p> <p>[2] Etard O and Reichenbach T. “Neural speech tracking in the theta and in the delta frequency band differentially encode clarity and comprehension of speech in noise” 2019. <em>J. Neurosci.</em> <strong>39</strong> 5750–9</p> <p>[3] Etard O and Reichenbach T. "EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise". Doi: 10.5281/zenodo.7086208</p>
Choice-dependent delta-band neural trajectory during semantic category decision making in the human brain
<p>The dataset and code provided correspond to Manuscript Number: ISCIENCE-D-23-09408R1, titled "Choice-dependent delta-band neural trajectory during semantic category decision making in the human brain."</p> <p>This dataset comprises delta and alpha filtered data from a total of 19 participants. Each .mat file contains 800 cells, representing the number of trials. Each cell contains a matrix of size 128 x 1750. Here, 128 denotes the number of EEG channels, and 1750 represents the number of time points. Time points are sampled from -1 s to 2.5 s relative to the onset of the first stimulus, with a 2ms interval.<br><br></p> <p>The participants' behavioral data are stored in separate .mat files for each run (or block). Upon loading these files, a struct named "data" is loaded, containing six variables, each representing a 1 x 200 vector:</p> <ol> <li> <p>Cat1: Represents the category of the first stimulus. It takes a value of 1 for animate words and 2 for inanimate words.</p> </li> <li> <p>Cat2: Denotes the category of the second stimulus. It is assigned 1 for animate words and 2 for inanimate words.</p> </li> <li> <p>Same: Indicates the correct response for each trial. A value of 1 signifies a match between the categories of Stimulus 1 and Stimulus 2, while 2 indicates a non-match.</p> </li> <li> <p>Resp: Records the participant's decision for each trial. A value of 1 denotes a match between the categories of Stimulus 1 and Stimulus 2, whereas 2 represents a non-match.</p> </li> <li> <p>RT: Represents the participant's response time in seconds for each trial.</p> </li> <li> <p>Corr: Indicates the correctness of the participant's response. A value of 1 signifies a correct response, while 0 indicates an incorrect response.</p> </li> </ol> <p> </p>
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