IIST BCI Dataset-3 for 100 Malayalam Words

Citation Author(s):
Parvathy
S S
A. J College of Science and Technology, Thonnakkal
Nancy
Sunil
A. J College of Science and Technology, Thonnakkal
Shubham
Tayade
Indian Institute of Space Science and Technology(IIST), Thiruvananthapuram
Chittaloori
Likhitha
Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh
S.
Sumitra
Indian Institute of Space Science and Technology(IIST), Thiruvananthapuram
B. S.
Manoj
Indian Institute of Space Science and Technology(IIST), Thiruvananthapuram
Submitted by:
PARVATHY S
Last updated:
Thu, 05/09/2024 - 05:07
DOI:
10.21227/wa4h-cr10
Data Format:
.txt
License:
Creative Commons Attribution
2789 Views
Categories:
Signal Processing
Machine Learning
Biomedical and Health Sciences
Neuroscience
Brain
Keywords:
Open BCI, Malayalam dataset, EEG, 10-20 System, Brain, neurodegenerative disease, classification
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Abstract 

This paper introduces a dataset capturing brain signals generated by the recognition of 100 Malayalam words, accompanied by their English translations. The dataset encompasses recordings acquired from both vocal and sub-vocal modalities for the Malayalam vocabulary. For the English equivalents, solely vocal signals were collected. This dataset is created to help Malayalam speaking patients with neuro-degenerative diseases. This dataset not only contributes to the advancement of braincomputer interface technology but also holds promise in fostering effective communication solutions for individuals with restricted verbal abilities.

Instructions: 

The raw dataset comprises text documents, with EEG samples stored as files containing values separated by commas and arranged in rows and columns. Each row represents a specific sample.

The first column denoting the sample index.
Columns 2 to 9 containing EEG recordings from eight selected channels.
Columns ranging from 10 to 22 and 24, may hold supplementary or unimportant data.
The 23rd column typically represents time in a raw, unprocessed format.
25th column displays timestamps in the format "YearMonth-Day Hour:Minute:Second". These timestamps provide precise temporal information for each sample, aiding in synchronization with external events or measurements.

Alongside EEG data, the text documents may contain metadata or supplementary information relevant to the recordings. This structured format facilitates efficient processing and analysis of EEG data for various research or clinical applications.

Data Descriptor Article DOI: 
https://doi.org/10.36227/techrxiv.171259992.21826294/v1

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