Electroencephalogram Analysis Method to Detect Unspoken Answers to Questions Using Multistage Neural Networks

被引:0
作者
Ito, Shin-Ichi [1 ]
Ito, Momoyo [1 ]
Fukumi, Minoru [1 ]
机构
[1] Tokushima Univ, Grad Sch Technol Ind & Social Sci, Tokushima 7708506, Japan
基金
日本学术振兴会;
关键词
Answer to question; convolutional neural networks; electroencephalogram; multistage neural networks; personal model; support vector machine; COMMON SPATIAL-PATTERNS; SINGLE-TRIAL EEG; EMOTION RECOGNITION; DEEP; BCI; CLASSIFICATION; EXTRACTION; IMPROVE; SIGNALS; ICA;
D O I
10.1109/ACCESS.2023.3339665
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Brain-computer interfaces (BCI) facilitate communication between the human brain and computational systems, additionally offering mechanisms for environmental control to enhance human life. The current study focused on the application of BCI for communication support, especially in detecting unspoken answers to questions. Utilizing a multistage neural network (MSNN) replete with convolutional and pooling layers, the proposed method comprises a threefold approach: electroencephalogram (EEG) measurements, EEG feature extraction, and answer classification. The EEG signals of the participants are captured as they mentally respond with "yes" or "no" to the posed questions. Feature extraction was achieved through an MSNN composed of three distinct convolutional neural network models. The first model discriminates between the EEG signals with and without discernible noise artifacts, whereas the subsequent two models are designated for feature extraction from EEG signals with or without such noise artifacts. Furthermore, a support vector machine is employed to classify the answers to the questions. The proposed method was validated via experiments using authentic EEG data. The mean and standard deviation values for sensitivity and precision of the proposed method were 99.6% and 0.2%, respectively. These findings demonstrate the viability of attaining high accuracy in a BCI by preliminarily segregating the EEG signals based on the presence or absence of artifact noise and underscore the stability of such classification. Thus, the proposed method manifests prospective advantages of separating EEG signals characterized by noise artifacts for enhanced BCI performance.
引用
收藏
页码:137151 / 137162
页数:12
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