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EEG-based attention type classification

Published on:

6 November 2023

Primary Category:

Human-Computer Interaction

Paper Authors:

Dhruv Verma,

Sejal Bhalla,

S. V. Sai Santosh,

Saumya Yadav,

Aman Parnami,

Jainendra Shukla


Key Details

Proposes AttentioNet, a novel CNN + self-attention model for EEG-based attention classification

Conducts study with 20 subjects performing neuropsych tests eliciting 5 attention types

AttentioNet achieves 92.3% accuracy in classifying attention types from EEG data

Transfer learning personalization handles EEG signal variability between subjects

Outperforms EEGnet baseline, showing promise despite dataset limitations

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AI generated summary

EEG-based attention type classification

This paper proposes AttentioNet, a novel deep learning approach using EEG data to classify attention into 5 types: selective, sustained, divided, alternating, and relaxed. A study of 20 subjects performing neuropsych tests eliciting different attention states was conducted. AttentioNet, a CNN with self-attention, achieved 92.3% accuracy in classifying attention types. Personalization via transfer learning addressed EEG signal variability. Results show AttentioNet outperforms EEGnet, confirming its effectiveness despite dataset limitations.

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