Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning

Article Subjects > Engineering Europe University of Atlantic > Research > Articles and books
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production Abierto Inglés The perception and recognition of objects around us empower environmental interaction. Harnessing the brain’s signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects’ responses to rapid-event visual stimuli and highlights the major factors that contribute to low accuracy in the EEG visual classification task.The proposed multi-class, multi-channel model integrates feature fusion to handle complex, non-stationary signals. This model is applied to the largest publicly available EEG dataset for visual classification consisting of 40 object classes, with 1000 images in each class. Contemporary state-of-the-art studies in this area investigating a large number of object classes have achieved a maximum accuracy of 17.6%. In contrast, our approach, which integrates Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves a classification accuracy of 33.17% for 40 classes. These results demonstrate the potential of EEG signals in advancing EEG visual classification and offering potential for future applications in visual machine models. metadata Rehman, Madiha and Anwer, Humaira and Garay, Helena and Alemany Iturriaga, Josep and Díez, Isabel De la Torre and Siddiqui, Hafeez ur Rehman and Ullah, Saleem mail UNSPECIFIED, UNSPECIFIED, helena.garay@uneatlantico.es, josep.alemany@uneatlantico.es, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED (2024) Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning. Sensors, 24 (21). p. 6965. ISSN 1424-8220

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Official URL: http://doi.org/10.3390/s24216965

Abstract

The perception and recognition of objects around us empower environmental interaction. Harnessing the brain’s signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects’ responses to rapid-event visual stimuli and highlights the major factors that contribute to low accuracy in the EEG visual classification task.The proposed multi-class, multi-channel model integrates feature fusion to handle complex, non-stationary signals. This model is applied to the largest publicly available EEG dataset for visual classification consisting of 40 object classes, with 1000 images in each class. Contemporary state-of-the-art studies in this area investigating a large number of object classes have achieved a maximum accuracy of 17.6%. In contrast, our approach, which integrates Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves a classification accuracy of 33.17% for 40 classes. These results demonstrate the potential of EEG signals in advancing EEG visual classification and offering potential for future applications in visual machine models.

Item Type:	Article
Uncontrolled Keywords:	BCI; EEG; visual classification; rapid-event design; block design
Subjects:	Subjects > Engineering
Divisions:	Europe University of Atlantic > Research > Articles and books Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production University of La Romana > Research > Scientific Production
Date Deposited:	31 Oct 2024 23:30
Last Modified:	31 Oct 2024 23:30
URI:	https://repositorio.uneatlantico.es/id/eprint/14951

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Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning

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