计算机科学
脑电图
变压器
特征提取
人工智能
脑-机接口
警惕(心理学)
建筑
利用
机器翻译
机器学习
语音识别
模式识别(心理学)
工程类
认知心理学
视觉艺术
艺术
电压
电气工程
精神科
计算机安全
心理学
作者
Victor Delvigne,Hazem Wannous,Jean-Philippe Vandeborre,Laurence Ris,Thierry Dutoit
标识
DOI:10.1109/icpr56361.2022.9956610
摘要
For many years now, understanding the brain mechanism has been a great research subject in many different fields. Brain signal processing and especially electroencephalogram (EEG) has recently known a growing interest both in academia and industry. One of the main examples is the increasing number of Brain-Computer Interfaces (BCI) aiming to link brains and computers. In this paper, we present a novel framework allowing us to retrieve the attention state, i.e degree of attention given to a specific task, from EEG signals. While previous methods often consider the spatial relationship in EEG through electrodes and process them in recurrent or convolutional based architecture, we propose here to also exploit the time and frequency information with a transformer-based network that has already shown its supremacy in many machine-learning (ML) related studies, e.g. machine translation. In addition to this novel architecture, an extensive study on the feature extraction methods, frequential bands and temporal windows length has also been carried out. The proposed network has been trained and validated on two public datasets and achieves higher results compared to state-of-the-art models. As well as proposing better results, the framework could be used in real applications, e.g. Attention Deficit Hyperactivity Disorder (ADHD) symptoms or vigilance during a driving assessment.
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