TY - JOUR
T1 - Toward the enhancement of affective brain-computer interfaces using dependence within EEG series
AU - Pei, Yu
AU - Zhao, Shaokai
AU - Xie, Liang
AU - Ji, Bowen
AU - Luo, Zhiguo
AU - Ma, Chuang
AU - Gao, Kun
AU - Wang, Xiaomin
AU - Sheng, Tingyu
AU - Yan, Ye
AU - Yin, Erwei
N1 - Publisher Copyright:
© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
PY - 2025/4/1
Y1 - 2025/4/1
N2 - In recent years, electroencephalogram (EEG)-based affective brain-computer interfaces (aBCI) has made remarkable advances. Objective. However, a subtle but crucial problem caused by the sliding window method has long been overlooked, which is the serious quantity mismatch between stimuli and short-term EEG frames. This may be an important factor limiting the performance of aBCIs. Approach. We refer to this mismatch as the quantity-independence imbalance (Q/I imbalance) and we propose the weak independence hypothesis to explain the mismatch. To validate this hypothesis and explore the effects of the Q/I imbalance on short-term EEG frames, we design four experiments from four perspectives, which are visualization, cross-validation, randomness test, and redundancy test. Main results. Inspired by validation experiments, we propose an inference correction (IC) method to enhancing the emotional predictions by leveraging the majority of the classifier’s outputs. The proposed IC method is evaluated on two datasets involving 60 subjects using both intra-subject and inter-subject validation protocols. Our IC achieves a significant improvement of 14.97% in classification accuracy. Significance. This study promotes the understanding of the time-dependent nature of EEG signals in aBCI.
AB - In recent years, electroencephalogram (EEG)-based affective brain-computer interfaces (aBCI) has made remarkable advances. Objective. However, a subtle but crucial problem caused by the sliding window method has long been overlooked, which is the serious quantity mismatch between stimuli and short-term EEG frames. This may be an important factor limiting the performance of aBCIs. Approach. We refer to this mismatch as the quantity-independence imbalance (Q/I imbalance) and we propose the weak independence hypothesis to explain the mismatch. To validate this hypothesis and explore the effects of the Q/I imbalance on short-term EEG frames, we design four experiments from four perspectives, which are visualization, cross-validation, randomness test, and redundancy test. Main results. Inspired by validation experiments, we propose an inference correction (IC) method to enhancing the emotional predictions by leveraging the majority of the classifier’s outputs. The proposed IC method is evaluated on two datasets involving 60 subjects using both intra-subject and inter-subject validation protocols. Our IC achieves a significant improvement of 14.97% in classification accuracy. Significance. This study promotes the understanding of the time-dependent nature of EEG signals in aBCI.
KW - EEG
KW - affective brain-computer interfaces
KW - emotion recognition
KW - randomness test
KW - sliding window method
UR - http://www.scopus.com/inward/record.url?scp=105002012993&partnerID=8YFLogxK
U2 - 10.1088/1741-2552/adbfc0
DO - 10.1088/1741-2552/adbfc0
M3 - 文章
C2 - 40073454
AN - SCOPUS:105002012993
SN - 1741-2560
VL - 22
JO - Journal of Neural Engineering
JF - Journal of Neural Engineering
IS - 2
M1 - 026038
ER -