EEG

We developed IIST BCI Dataset-9, a novel EEG-based Brain-Computer Interface (BCI)
dataset to improve wheelchair control systems using Malayalam dialect variations. BCI
systems help people with motor disabilities by allowing them to control devices using brain
signals. The limited number of BCI datasets in Indian languages makes it harder for native
speakers to use these systems. To address this, we created a dataset with 15 Malayalam
words related to basic wheelchair commands like Forward, Backward, Go, Stop, Reverse,

Synthetic EEG Dataset for CNN-LSTM Training: Clean and Artifact-Contaminated Signals

This dataset consists of synthetically generated EEG and EMG signals designed for training Convolutional Neural Networks (CNNs) in artifact detection and removal. The dataset includes both clean EEG signals and EEG signals contaminated with simulated EMG artifacts from various sources.

This repository contains resources for EEG data processing and cognitive load recognition using a Multi-Head Attention EEGNet model. It includes original EEG data, MATLAB code for preprocessing, and Python code for classification.

With the ethics approval obtained from our institution, this study acquired 30 subjects aged between 18 to 29 to conduct research. Informed written consents were attained from all participants. The selection of participants follows a standardized and rigorous protocol that they have to meet the following requirements:

Abstract

PassengerEEG is a brain-signal dataset designed to study how human passengers perceive and cognitively respond to potential traffic hazards in highly automated vehicles (AVs). As AVs increasingly replace human drivers, understanding passenger cognition becomes essential for improving vehicle safety and adaptive decision-making.

This study investigated neural mechanisms underlying working memory by employing a visual n-back task with graded cognitive load (0-back to 3-back). Ten healthy volunteers (6 males, 4 females; mean age 23.3 ± 0.9 years) participated, performing a spatial matching task where they judged whether the current position of a displayed square matched the position presented n trials earlier, responding via keypress ("V" for match, "N" for non-match).

Synthetic Epileptic Spike EEG Database (SESED-WUT)

The database contains EEG, EMG, and EOG signals with artificially generated epileptic spikes. The recordings were performed using the g.USBamp 2.0 amplifier. Data were collected from 5 EEG channels (C3, Cz, C4, Fz, Fp1), 1 EOG channel (VEOG), and 3 EMG channels (Nape, Cheek, Jaw). The signals were sampled at 256 Hz and processed with a bandpass filter (0.1–100 Hz) and a notch filter (48–52 Hz).

The dataset consists of EEG recorded non-invasively from five participants while they are performing a handwriting imagery task, in which each participant was instructed to imagine the process of handwriting the 26 English letters. Major preprocessing steps have been conducted, including large-amplitude artifact removal using Independent Component Analysis and bandpass filtering between 0.1-45 Hz.

We developed a unique and valuable dataset specifically for advancing Brain-Computer Interface (BCI) systems by recording brain activity from a dedicated volunteer. The participant was asked to pronounce 100 carefully selected Malayalam words, along with their English translations, which were chosen for their relevance to astronauts during human space missions. The volunteer pronounced these words both vocally and subvocally, each word being repeated 50 times. Non-invasive Electroencephalography (EEG) sensors were employed to capture the brain activity associated with these tasks.

In this study, we collected EEG and EMG data from 16 subjects during the MI process and constructed a homemade MI-hBCI dataset. The participants included 10 males (mean age: 22.3±3.1 years) and 6 females (mean age: 22.1±2.4 years). All the subjects were right-handed, had normal vision, and had no motor impairment; all the participants signed a consent form and were informed of the experimental procedure and precautions before the experiment.

This dataset contains EEG error-related potential signals elicited by humans while observing an AI agent play an atari-based maze game.