Title: A deep learning approach in brain-computer interaction for augmentative and alternative communication

Authors: Yubin Liu

Addresses: Department of Computer Science, Tangshan Normal University, Tangshan, 063000, China

Abstract: The electroencephalography classification is the primary aspect of brain-computer systems. The changes there are two main concerns. Firstly, conventional approaches do not use multimodal knowledge to their fullest degree. Second, it is almost unlikely to obtain the rule-based EEG repositories as genetic information processing is complex, and metadata accuracy is expensive. In this sense, researchers suggest a new approach named deep learning-based brain-computer interaction (DLBCI) for augmentative and alternative communication to profound transfer learning to address these issues. Initially, these model perceptual activities based on EEG signals, using the EEG input images characterisation, which is intended to retain a standardised description of multimodal ECG signals. Secondly, this model develops a deep-scope transmission of knowledge through joint operations, including an opponent's infrastructure and an incredibly unique transfer function. The proposed framework for EEG classification problems, such as strength and precision, has many economic benefits in experimentation.

Keywords: deep learning; brain-computer interaction; augmentative and alternative communication; multimodal signal.

DOI: 10.1504/IJICT.2024.137204

International Journal of Information and Communication Technology, 2024 Vol.24 No.2, pp.181 - 199

Received: 18 Mar 2021
Accepted: 20 Jun 2021
Published online: 05 Mar 2024 *

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