Charu Chauhan

Indian Institute of Space Science and Technology

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B. S. Manoj

Indian Institute of Technology Roorkee

Nancy Sunil

Indian Institute of Technology Indore

S S Parvathy

Indian Institute of Technology Indore

S. Sumitra

Indian Institute of Technology Roorkee

Anusha Jain

Medi-Caps University

Sutanu Chakraborti

Indian Institute of Technology Madras

P. S. Chandramohanan Nair

Institute of Science and Technology

Shubham Tayade

Indian Institute of Space Science and Technology

G GAYATHRI DEVI

MNR Dental College and Hospital

Himanshu Tiwari

Sharda University

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Indian Institute of Technology Roorkee

Indian Institute of Space Science and Technology

Indian Institute of Technology Indore

Institute of Science and Technology

Chhattisgarh Swami Vivekanand Technical University

Indian Institute of Technology Madras

Medi-Caps University

M. Kumarasamy College of Engineering

University of California, San Diego

Atal Bihari Vajpayee Indian Institute of Information Technology and Management

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IIST BCI Dataset-2 for Selected Common Marathi Words

Shubham Tayade S S Parvathy Nancy Sunil Charu Chauhan S. Sumitra

To solve problems of neurodegenerative disorder patients, Brain-Computer Interface (BCI) based solutions require datasets relevant to the languages spoken by patients. BCI Research sometimes gets restricted due to the lack of datasets. For example, Marathi, a prominent language spoken by over 83 million people in India, lacks BCI datasets based on the language for research purposes. To tackle this gap, we created a dataset comprising of Electroencephalograph (EEG) signal samples of selected common Marathi words. EEG samples were captured using the OpenBCI Cyton device for constructing a dataset by volunteers who speak commonly used Marathi words. The dataset contains EEG recordings involving volunteers pronouncing commonly used Marathi words. It encompasses three main categories: (i) Utterances of Marathi words (Vocal), (ii) English translations of these Marathi words (Vocal), and (iii) Silent pronunciation (sub-vocalization) of the Marathi words. We compiled data for 100 distinct words, each with recordings for these three categories. Ten trials were conducted for each phrase. This dataset is valuable for developing BCI solutions to assist Marathi-speaking patients with neurodegenerative diseases. BCI solutions using Machine Learning (ML) classifiers and Deep Learning methods can be used to translate EEG signals into Marathi words, both vocal and sub-vocal.

Marathi

10.36227/techrxiv.171043118.80448751/v1

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IIST BCI Dataset-1 for Selected Common Malayalam Words

P. S. Chandramohanan Nair S S Parvathy Nancy Sunil Anurag Mukati Charu Chauhan

Designing Brain Computer Interfaces (BCIs), for helping patients, needs appropriate datasets which are relevant for the language of the patients. There exists a significant shortage of datasets for Indian languages that can be used for BCI research. Malayalam is a prominent south Indian language spoken by more than 34 million people, yet, there exist no BCI datasets for research. We address this issue by creating a dataset for selected Malayalam words by collecting Electro Encephalograph (EEG) signal samples. Our dataset was created by generating EEG samples using the OpenBCI Cyton device when the commonly used Malayalam words were spoken by a volunteer. The created dataset consists of three major types of data: (i) EEG data for spoken Malayalam words, (ii) EEG data for the spoken English words which were closest to the English translation of the corresponding Malayalam words, and (iii) EEG data for sub-vocal (silent) pronunciation of the Malayalam words. We created the dataset for 26 words where each of these words had been recorded for the above mentioned three types. For each word, 10 EEG samples over 8 channels were recorded. This dataset is useful for developing BCI solutions for patients suffering from neuro-degenerative diseases by developing Machine Learning (ML) classifiers for translating EEG-signals to Malayalam words, vocal or sub-vocal, especially considering the scarcity of datasets available in Indian languages.

Malayalam

10.36227/techrxiv.170473894.42689961/v1

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ANALYSIS ON HAND GESTURE RECOGNITION USING ARTIFICIAL NEURAL NETWORK

Ethics and Information Technology (2020)

Hemant Maurya Charu Chauhan Himanshu Tiwari Anusha Jain

10.26480/etit.02.2020.127.133

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Conceptualizing Curse of Dimensionality with Parallel Coordinates

Proceedings of the AAAI Conference on Artificial Intelligence (2016)

G GAYATHRI DEVI Charu Chauhan Sutanu Chakraborti

We report on a novel use of parallel coordinates as a pedagogical tool for illustrating the non-intuitive properties of high dimensional spaces with special emphasis on the phenomenon of Curse of Dimensionality. Also, we have collated what we believe to be a representative sample of diverse approaches that exist in literature to conceptualize the Curse of Dimensionality. We envisage that the paper will have pedagogical value in structuring the way Curse of Dimensionality is presented in classrooms and associated lab sessions.

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10.1609/aaai.v30i1.9855

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