BISNN: bio-information-fused spiking neural networks for enhanced EEG-based emotion recognition

Alzhrani W, Doborjeh M, Doborjeh Z, et al (2021) Emotion recognition and understanding using EEG data in a brain-inspired spiking neural network architecture. In: IJCNN, pp 1–9

Bu T, Fang W, Ding J, et al (2022) Optimal ANN-SNN conversion for high-accuracy and ultra-low-latency spiking neural networks. In: ICLR

Cai W, Sun H, Liu R et al (2024) A spatial-channel-temporal-fused attention for spiking neural networks. IEEE Trans Neural Networks Learn Syst 35(10):14315–14329

Cdv G, Jhs A, Antelis JM et al (2020) Spiking neural networks applied to the classification of motor tasks in EEG signals. Neural Netw 122:130–143

Chen S, Tang J, Zhu L et al (2023) A multi-stage dynamical fusion network for multimodal emotion recognition. Cogn Neurodyn 17(3):671–680

PubMed Google Scholar

Chen T, She C, Wang L et al (2024) Memristive leaky integrate-and-fire neuron and learnable straight-through estimator in spiking neural networks. Cogn Neurodyn 18(5):3075–3091

PubMed Google Scholar

Degirmenci M, Yuce YK, Perc M et al (2023) Statistically significant features improve binary and multiple motor imagery task predictions from eegs. Front Hum Neurosci 17:1223307

PubMed PubMed Central Google Scholar

Degirmenci M, Yuce YK, Perc M et al (2024) EEG-based finger movement classification with intrinsic time-scale decomposition. Front Hum Neurosci 18:1362135

PubMed PubMed Central Google Scholar

Deng S, Gu S (2021) Optimal conversion of conventional artificial neural networks to spiking neural networks. In: ICLR

Fang H, Shrestha A, Zhao Z, et al (2020) Exploiting neuron and synapse filter dynamics in spatial temporal learning of deep spiking neural network. In: IJCAI, pp 2799–2806

Fang W, Yu Z, Chen Y, et al (2021) Incorporating learnable membrane time constant to enhance learning of spiking neural networks. In: ICCV, pp 2661–2671

Gao Z, Dang W, Wang X et al (2021) Complex networks and deep learning for EEG signal analysis. Cogn Neurodyn 15(3):369–388

PubMed Google Scholar

Gong P, Wang P, Zhou Y et al (2023) A spiking neural network with adaptive graph convolution and lstm for EEG-based brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 31:1440–1450

Goshvarpour A, Goshvarpour A (2024) EEG emotion recognition based on an innovative information potential index. Cogn Neurodyn 18(5):2177–2191

PubMed Google Scholar

Huang C, Resnik A, Celikel T et al (2016) Adaptive spike threshold enables robust and temporally precise neuronal encoding. PLoS Comput Biol 12(6):e1004984

PubMed PubMed Central Google Scholar

Jenke R, Peer A, Buss M (2014) Feature extraction and selection for emotion recognition from EEG. IEEE Trans Affect Comput 5(3):327–339

Jin J, Miao Y, Daly I et al (2019) Correlation-based channel selection and regularized feature optimization for MI-based BCI. Neural Netw 118:262–270

PubMed Google Scholar

Koelstra S, Muhl C, Soleymani M et al (2011) DEAP: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput 3(1):18–31

Lawhern VJ, Solon AJ, Waytowich NR et al (2018) EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J Neural Eng 15(5):056013

PubMed Google Scholar

Li P, Liu H, Si Y et al (2019) EEG based emotion recognition by combining functional connectivity network and local activations. IEEE Trans Biomed Eng 66(10):2869–2881

PubMed Google Scholar

Li X, Luo S, Xue F (2020) Effects of synaptic integration on the dynamics and computational performance of spiking neural network. Cogn Neurodyn 14(3):347–357

PubMed PubMed Central Google Scholar

Li C, Li P, Zhang Y et al (2023) Effective emotion recognition by learning discriminative graph topologies in EEG brain networks. IEEE Trans Neural Networks Learn Syst 35(8):10258–10272

Li Y, Fan L, Shen H et al (2024) HR-SNN: an end-to-end spiking neural network for four-class classification motor imagery brain-computer interface. IEEE Trans Cognitive Develop Syst 16(6):1955–1968

Luo Y, Fu Q, Xie J et al (2020) EEG-based emotion classification using spiking neural networks. IEEE Access 8:46007–46016

Luo C, Li F, Li P et al (2022) A survey of brain network analysis by electroencephalographic signals. Cogn Neurodyn 16(1):17–41

PubMed Google Scholar

Maas AL, Hannun AY, Ng AY, et al (2013) Rectifier nonlinearities improve neural network acoustic models. In: ICML, p 3

Maass W (1997) Networks of spiking neurons: the third generation of neural network models. Neural Netw 10(9):1659–1671

Mao Y, Jin J, Xu R et al (2021) The influence of visual attention on the performance of a novel tactile p300 brain-computer interface with cheeks-stim paradigm. Int J Neural Syst 31(04):2150004

PubMed Google Scholar

Miao Y, Jin J, Daly I et al (2021) Learning common time-frequency-spatial patterns for motor imagery classification. IEEE Trans Neural Syst Rehabil Eng 29:699–707

PubMed Google Scholar

Peng R, Du Z, Zhao C, et al (2024) Multi-branch mutual-distillation transformer for EEG-based seizure subtype classification. In: IEEE Transactions on Neural Systems and Rehabilitation Engineering

Perez-Nieves N, Leung VC, Dragotti PL et al (2021) Neural heterogeneity promotes robust learning. Nat Commun 12:5791

CAS PubMed PubMed Central Google Scholar

Qiu H, Ning M, Song Z et al (2024) Self-architectural knowledge distillation for spiking neural networks. Neural Netw 178:106475

PubMed Google Scholar

Shen G, Zhao D, Dong Y et al (2023) Brain-inspired neural circuit evolution for spiking neural networks. Proc Natl Acad Sci 120(39):e2218173120

CAS PubMed PubMed Central Google Scholar

Subasi A, Tuncer T, Dogan S et al (2021) EEG-based emotion recognition using tunable q wavelet transform and rotation forest ensemble classifier. Biomed Signal Process Cont 68:102648

Sun H, Cai W, Yang B et al (2023) A synapse-threshold synergistic learning approach for spiking neural networks. IEEE Trans Cognitive Develop Syst 16(2):544–558

Sun H, Liu R, Cai W et al (2024) Reliable object tracking by multimodal hybrid feature extraction and transformer-based fusion. Neural Netw 178:106493

PubMed Google Scholar

Tao W, Li C, Song R et al (2020) EEG-based emotion recognition via channel-wise attention and self attention. IEEE Trans Affect Comput 14(1):382–393

Tripathi S, Acharya S, Sharma R, et al (2017) Using deep and convolutional neural networks for accurate emotion classification on DEAP data. In: AAAI, pp 4746–4752

Wu Y, Deng L, Li G et al (2018) Spatio-temporal backpropagation for training high-performance spiking neural networks. Front Neurosci 12:331

PubMed PubMed Central Google Scholar

Wu Y, Deng L, Li G, et al (2019) Direct training for spiking neural networks: Faster, larger, better. In: AAAI, pp 1311–1318

Wu S, Xu X, Shu L, et al (2017) Estimation of valence of emotion using two frontal EEG channels. In: BIBM, pp 1127–1130

Xu F, Pan D, Zheng H et al (2024) EESCN: a novel spiking neural network method for EEG-based emotion recognition. Comput Methods Programs Biomed 243:107927

PubMed Google Scholar

Xu Q, Li Y, Shen J, et al (2023) Constructing deep spiking neural networks from artificial neural networks with knowledge distillation. In: CVPR, p 7886–7895

Yan Z, Zhou J, Wong WF (2022) EEG classification with spiking neural network: smaller, better, more energy efficient. Smart Health 24:100261

Ye M, Chen CP, Zhang T (2022) Hierarchical dynamic graph convolutional network with interpretability for EEG-based emotion recognition. IEEE Trans Neural Networks Learn Syst. https://doi.org/10.1109/TNNLS.2022.3225855

Yedjour H, Yedjour D (2024) A spatiotemporal energy model based on spiking neurons for human motion perception. Cogn Neurodyn 18(4):2015–2019

PubMed Google Scholar

Zhang A, Li X, Gao Y et al (2022) Event-driven intrinsic plasticity for spiking convolutional neural networks. IEEE Trans Neural Networks Learn Syst 33(5):1986–1995

Zhao G, Ge Y, Shen B et al (2017) Emotion analysis for personality inference from EEG signals. IEEE Trans Affect Comput 9(3):362–371

Zheng WL, Lu BL (2015) Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans Auton Ment Dev 7(3):162–175

Zheng H, Zheng Z, Hu R et al (2024) Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics. Nat Commun 15(1):277

CAS PubMed PubMed Central Google Scholar

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