Fusion with EEG signals and Images for closed or open eyes detection using deep learning

EEG technology has been used in many areas and systems such as games and electric wheelchairs. Despite their maturity and the availability of low-cost easy-to-use EEG sensors, the risk of error command still remains. To tackle this problem, merging EEG sensor data with another sensor can be an inter...

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Bibliographic Details
Main Authors: Zaway, Lassaad, Ben Amor, Nader, Ktari, Jalel, Jallouli, Mohamed, Chrifi-Alaoui, Larbi, Delahoche, Laurent
Format: Conference Proceeding
Language:English
Subjects:
Blink detection
Convolutional Neural Network (CNN)
Deep learning
Electroencephalogram (EEG)
Electroencephalography
Games
Long-Short Term Memory (LSTM)
Merging
Neural networks
Visualization
Wheelchairs
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Summary:EEG technology has been used in many areas and systems such as games and electric wheelchairs. Despite their maturity and the availability of low-cost easy-to-use EEG sensors, the risk of error command still remains. To tackle this problem, merging EEG sensor data with another sensor can be an interesting solution. In this work, we address this hypothesis by merging EEG-based blink detection with facial images captured by cameras using a deep learning algorithm, enabling the simultaneous analysis of both neural and visual aspects of blinks. This fusion not only enhances detection accuracy but also allows for assessing blink-related cognitive workload and emotional responses in real time.
ISSN:2832-823X
DOI:10.1109/DTTIS59576.2023.10348220