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10 results for “EOG”
Raw EEG-EOG data used in the publication "Auditory Electrooculogram-based Communication System for ALS Patients in Transition from Locked-in to Complete Locked-in State"
<p>The dataset includes raw EEG and EOG recordings during BCI experiments for three patients: p11, p13, p15, and p16. The structure of the dataset is the following: patient/visit/day.</p> <p>The experiment is described in detail in the publication "Auditory Electrooculogram-based Communication System for ALS Patients in Transition from Locked-in to Complete Locked-in State". The correspondence between raw file and BCI session is reported in the attached pdf file "Supplementary Table S5 Session to Raw File Recordings Correspondence".</p> <p>The datasets include EEG and EOG channels. The data are raw (i.e. non filtered and non processed). Data have been acquired with a sampling rate of 500Hz using active electrodes and the amplifier V-Amp DC (Brain Products, Germany). EOG channels are labeled EOGU, EOGD, EOGR, EOGL namely for EOG up, down, right, left; the location in the 10-20 system are respectively SO1, IO1, LO1, LO2.</p> <p>The data are marked with triggers: for each session two markers indicate start and end of the session; for each trial markers indicate start of baseline, start of presentation of question, start of response time, start of feedback. Each trial was marked in a different way if it was a yes trial belonging to a training or feedback session, a no trial belonging to a training or feedback session, or a trial belonging to a speller session. The markers that have been used are the following:<br> <strong>start</strong> 9<br> <em> yes no speller</em><br> <strong>baseline</strong> 10 11 12<br> <strong>presentation</strong> 5 6 7<br> <strong>response</strong> 4 8 13<br> <strong>feedback</strong> 1 2 3</p> <p><strong>end</strong><strong> </strong> 15</p>
Eye-tracking (EOG) Data
<p>Dataset taken from https://sites.google.com/site/consensusmotifs/</p> <p>Stored on Zenodo as backup for Stumpy Consensus Motif Search</p>
PVT dataset to EOG_RAW
<p>EOG_Raw</p> <p>EOG_Raw data from J!NS MEME glasses, self-assessments, and PVT recordings collected in-the-wild</p> <p>If you are using this data and publish work of any kind, please make sure to cite the following paper: <a href="https://dl.acm.org/citation.cfm?id=3300694">https://dl.acm.org/citation.cfm?id=3300694</a></p> <p>DOWNLOAD INSTRUCTIONS:</p> <p>EOG_Raw Data archive is very big (10.2GB)</p> <p>Please download it from:</p> <p><a href="https://zenodo.org/record/2532900/files/EOG%20Raw%20Data.zip?download=1">https://zenodo.org/record/2532900/files/EOG%20Raw%20Data.zip?download=1</a></p>
Raw EOG Data
<p>This dataset includes all electrooculography recordings for 16 users of J!NS MEME glasses over a 2 weeks period. The here uploaded dataset was used in the paper "Continuous Alertness Assessments: Using EOG Glasses to Unobtrusively Monitor Fatigue Levels In-The-Wild." In CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2019), May 4–9, 2019, Glasgow, Scotland Uk. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/ 3290605.3300694. </p>
fNIRS, EEG and EOG classification accuracy used to generate Fig 5
<p>fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient W, was used to generate Fig 5.</p>
fNIRS, EEG and EOG classification accuracy used to generate Fig 4
<p>fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient B, was used to generate Fig 4.</p>
fNIRS, EEG and EOG classification accuracy used to generate Fig 3
<p>fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient G, was used to generate Fig 3.</p>
fNIRS, EEG and EOG classification accuracy used to generate Fig 2
<p>fNIRS (functional near infrared spectroscopy), EEG (Electroencephalograph) and EOG (Electrooculogram) classification accuracy of different sessions performed by Patient F, was used to generate Fig 2.</p>
One-Sensor P300 EEG and Blink EOG from OpenBCI Cyton Board
<p>Two different systems have been developed in this study. An EEG control system based on P300 EP and an EOG system based on eye blinks. Both developed systems, the objects that the user can interact with are presented on a laptop screen using a GUI. This stimulation program was designed and developed in Python employing the PsychoPy package. The GUI, composed by a 3×3 matrix containing nine images describing the objects and control functions to be executed by users. The first two rows correspond to home devices, such as TVs, digital house locks or electric lights, which have a state that can be switched (e.g., on/off or up/down) by selecting it with the HMI. The last row has three possible phone calls: the emergency call (i.e., an SOS number) and two favourite contacts in their phone-book.</p><p>Two stimulation paradigms were assessed for the experiments. For the first paradigm, each element of the matrix is intensified one by one. This intensification is randomly performed, but all the elements are intensified the same number of times. Therefore, they will have the same probability of being intensified, that is, 𝑝=1/9. Moreover, an element cannot be intensified two consecutive times..For the second paradigm, instead of element by element, each row and column of the matrix is intensified. This is also done randomly, and they are intensified the same number of times. Therefore, the probability of one element being intensified at any given time is 𝑝=2/6=1/3.</p><p> </p><p>EEG and EOG data have been recorded using the OpenBCI Cyton Board with a sampling frequency of 250Hz. For EEG recordings, since the objective is to detect the P300 potential, the electrode for the input channel was located in the Cz position. Conversely, for EOG recordings, the electrode was placed at Fp2 in order to detect activity related to eye blinks. Both recording modes shared reference and ground electrodes, placed at the right mastoid (RM) and the left earlobe (A1), respectively. </p><p>The participant group in our experiments included a total of nine volunteers who agreed to collaborate in this research. The participants indicated that they did not have hearing or visual impairments. Informed consents were obtained from all the participants in order to employ their data in our study. The experiments were carried out in a sound-attenuated room where the participants were invited to sit in a comfortable chair while focusing their attention on a 15.6-inch laptop screen where stimuli were presented. Recording sessions for each participant were divided into four independent experiments, one per each stimulation paradigm and recording mode that is: for the 1-by-1 paradigm with EEG signals, for the row/column paradigm with EEG signals, for the 1-by-1 paradigm with EOG signals and for the row/column paradigm with EOG data. Eight runs were recorded for each experiment.</p><p>Each subject recording is stored in one separate folder, named s[number_subject], e.g., for subject 1: folder s1. Inside each subject folder we will find 32 folders corresponding with each experiment. The structure of the name is the following: subject_controlSignal_paradigm_date, eg, s1P3001by1-12-11-2020_12-57-15. Inside each experiment folder we will find 3 files. </p><ul><li>ExInfo : Contains data related to the subject, channel, paradigm and also the target element of that experiment.</li><li>Raw-STIMS: Contains the stimuli presentation for that experiment. It is important to note that each specific stimuli is represented by a code. In the 1-by-1 paradigm these codes range from 101 to 109 and represent each of the elements of the 3x3 matrix, from the upper left corner to the bottom right corner. In the row-column paradigm these codes range from 101 to 106. Codes 101-103 represents the rows of the matrix from top to bottom. Codes 104-106 represent the columns of the matrix from left to right.</li><li>Raw-EEG: Contains the EEG data recorded from the Cyton board. </li></ul><p>For more details about the recordings and the data plase see the related paper of this study and also the associated code:</p><p><a href="https://www.mdpi.com/1424-8220/21/6/2220">Sensors | Free Full-Text | Proposals and Comparisons from One-Sensor EEG and EOG Human-Machine Interfaces (mdpi.com)</a></p><p><a href="https://github.com/franciscolaport/Proposals-and-Comparisons-from-One-Sensor-EEG-and-EOG-Human-Machine-Interfaces">franciscolaport/Proposals-and-Comparisons-from-One-Sensor-EEG-and-EOG-Human-Machine-Interfaces (github.com)</a></p>
Audio files for A Dataset of EEG and EOG recordings from an Auditory EOG-based Communication System for Patients in Locked-In State
<ol> <li>Origin of the data</li> </ol> <p>These Audio files are related to the publication "A dataset of EEG and EOG from an auditory EOG-based communication system for patients in locked-in state" by Andres Jaramillo-Gonzalez, Shize Wu, Alessandro Tonin, Aygul Rana, Majid Khalili-Ardali, Niels Birbaumer, and Ujwal Chaudhary. To use the audio data, please contact Dr. Ujwal Chaudhary, as mentioned in the manuscript.</p> <ol> <li>Details of the data</li> </ol> <p>The description of the nature and details of the *.txt files are in the section "Data Records" of the manuscript. As mentioned in the manuscript, before the beginning of the study, at least 100 questions with known "yes" or "no" answers were formulated and recorded with a family member or caretaker's voice close to the patient. Each question with a "yes" answer is paired with a similar question with a "no" answer (e.g., "Paris is the capital of France" and "Berlin is the capital of France").</p> <p>Each question is saved as an audio file with an explicit identifier, a question with a "yes" answer is saved with a 001_NUMBER identifier, and a question with a "no" answer is saved with a 002_NUMBER identifier. The NUMBER label is composed of 5 digits; the first two determine the number of the patient, and the last three digits indicate the number of the question. The value of the label NUMBER is the same for a semantically paired sentence. In the example, "001_11007.wav" indicates a "yes" type of answer, question number 07 from P11. Sentences are then placed in a specific folder of the used laptop's storage, accessed and played by the communication system along with the sessions during sentence presentation.</p>
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