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An Efficient EEG Signal Analysis for Emotion Recognition Using FPGA

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posted on 2024-05-28, 14:29 authored by MR Ezilarasan, Man-Fai Leung
Electroencephalography (EEG), electromyography (EMG), galvanic skin response (GSR), and electrocardiogram (ECG) are among the techniques developed for collecting psychophysiological data from humans. This study presents a feature extraction technique for identifying emotions in EEG-based data from the human brain. Independent component analysis (ICA) was employed to eliminate artifacts from the raw brain signals before applying signal extraction to a convolutional neural network (CNN) for emotion identification. These features were then learned by the proposed CNN-LSTM (long short-term memory) algorithm, which includes a ResNet-152 classifier. The CNN-LSTM with ResNet-152 algorithm was used for the accurate detection and analysis of human emotional data. The SEED V dataset was employed for data collection in this study, and the implementation was carried out using an Altera DE2 FPGA development board, demonstrating improved performance in terms of FPGA speed and area optimization.

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Refereed

  • Yes

Volume

15

Issue number

6

Page range

301-301

Publication title

Information

ISSN

2078-2489

Publisher

MDPI AG

File version

  • Published version

Language

  • eng

Affiliated with

  • School of Computing and Information Science Outputs

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