  Classification of olfactory-induced electroencephalogram (EEG) signals has
shown great potential in many fields. Since different frequency bands within
the EEG signals contain different information, extracting specific frequency
bands for classification performance is important. Moreover, due to the large
inter-subject variability of the EEG signals, extracting frequency bands with
subject-specific information rather than general information is crucial.
Considering these, the focus of this letter is to classify the olfactory EEG
signals by exploiting the spectral-domain information of specific frequency
bands. In this letter, we present an olfactory EEG signal classification
network based on frequency band feature extraction. A frequency band generator
is first designed to extract frequency bands via the sliding window technique.
Then, a frequency band attention mechanism is proposed to optimize frequency
bands for a specific subject adaptively. Last, a convolutional neural network
(CNN) is constructed to extract the spatio-spectral information and predict the
EEG category. Comparison experiment results reveal that the proposed method
outperforms a series of baseline methods in terms of both classification
quality and inter-subject robustness. Ablation experiment results demonstrate
the effectiveness of each component of the proposed method.
