脑电图
人工智能
卷积神经网络
模式识别(心理学)
计算机科学
脑-机接口
特征(语言学)
感觉
语音识别
心理学
神经科学
语言学
哲学
作者
Han Gao,Shuo Zhao,Huiyan Li,Li Liu,Hengyang Wang,You Wang,Zhiyuan Luo,Jin Zhang,Guang Li
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:72: 1-10
被引量:4
标识
DOI:10.1109/tim.2023.3280529
摘要
Taste sensation recognition is a keystone for taste-related Brain-Computer Interface (BCI). A commonly used measurement of brain activity in response to specific stimulation is through electroencephalography (EEG) signals. However, it remains challenging to develop accurate and generalizable EEG-based measurement for human taste sensation. This paper proposes EEG-MSRNet, a novel fully convolutional neural network for EEG-based classification of basic taste sensations (blank, sour, sweet, bitter, salty, umami). Firstly, a multi-scale temporal convolution operation with residual learning is designed to extract features in different frequencies from the down-sampled EEG signals. Subsequently, a multi-scale spatial convolution operation represents the features in a cross-channel manner. Finally, a convolutional layer and global average pooling (GAP) layer are introduced to make predictions with the feature representation instead of the commonly used fully connected layers for classification. An experimental procedure is developed to acquire the EEG signals under taste stimulation. Comparison experiments and ablation studies have proved the stable and generalizable recognition performance of EEG-MSRNet on our self-collected EEG dataset. The results suggest that our EEG-based system with EEG-MSRNet is effective and generalizable for taste sensation recognition, which provides a powerful measurement for taste-related BCI such as taste disorder diagnosis and virtual taste.
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