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bbci_public

MATLAB toolbox for Brain-Computer Interfacing (BCI)

cogbeacon-multimodal_dataset_for_cognitive_fatigue

CogBeacon is a multi-modal dataset designed to target the effects of cognitive fatigue in human performance. The dataset consists of 76 sessions collected from 19 male and female users performing different versions of the Wisconsin Card Sorting Test (WCST); a popular cognitive test in experimental and clinical psychology designed to assess cognitive flexibility, reasoning and specific aspects of cognitive functioning. During each session we record and fully annotate, user's EEG functionality, facial keypoints, real-time self-reports on cognitive fatigue, as well as detailed information of the performance metrics achieved during the cognitive task (success rate, response time, number of errors etc.). Along with the dataset we provide a baseline Machine Learning analysis towards predicting cognitive fatigue and our multi-modal implementation of the WCST, to allow other researches expand or modify the functionalities of the CogBeacon data-collection framework. To our knowledge, this is the first multi-modal dataset specifically designed to assess cognitive fatigue.

cognitive-mental-workload-classification

Cognitive mental workload classification using Machine learning algorithms.

cognitive-mental-workload-eeg-data

It includes Cognitive workload data of 5 subjects collected using Emotiv Epoc+ device at BCI-HCI Labaratory in IIT Kharagpur.

cognitive-mental-workload-estimation-using-ml

An automated approach for task evaluation using EEG signals.

eeg-dataset--sequence-learning

EEG Dataset for the paper: Towards Predicting Task Performance from EEG Signals

emotion-recogniton-from-eeg-signals

Emotion Recognition from EEG Signals using the DEAP dataset with 86.4% accuracy. Applied multiple machine learning models and implemented various signal transforming algorithms like the DWT algorithm.

github-slideshow

A robot powered training repository :robot:

human-emotion-analysis-using-eeg-from-deap-dataset

Processed the DEAP dataset on basis of 1) PSD (power spectral density) and 2)DWT(discrete wavelet transform) features . Classifies the EEG ratings based on Arousl and Valence(high /Low)

pyeeg

Python + EEG/MEG = PyEEG

sequence-learning-cognive-task-eeg-performance-prediction

A machine learning approach to process EEG signals from the sequence task provided by https://github.com/TsiakasK/sequence-learning.git

simultaneous_eeg-nirs