Motion direction prediction through spike timing based on micro Capsnet networks

Zhang, Hualiang; Liu, Ji; Wang, BaoZeng; Dai, Jun; Lian, Jinling; Ke, Ang; Zhao, Yuwei; Zhou, Jin; Wang, Changyong

doi:10.1007/s11431-022-2072-9

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…The classification performance is perhaps not high enough for efficient detection in practice. Other channel selection methods [39,52], machine learning algorithms, and deep learning neural networks, such as EEGNet [53], spatial-temporal neural networks [54], and other deep neural networks [31,32,55,56], can be explored to improve the recognition performance for practical applications.…”

Section: Discussionmentioning

confidence: 99%

EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions

Lian,

Qiao,

Zhao

et al. 2023

Brain Sciences

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Although target detection based on electroencephalogram (EEG) signals has been extensively investigated recently, EEG-based target detection under weak hidden conditions remains a problem. In this paper, we proposed a rapid serial visual presentation (RSVP) paradigm for target detection corresponding to five levels of weak hidden conditions quantitively based on the RGB color space. Eighteen subjects participated in the experiment, and the neural signatures, including P300 amplitude and latency, were investigated. Detection performance was evaluated under five levels of weak hidden conditions using the linear discrimination analysis and support vector machine classifiers on different channel sets. The experimental results showed that, compared with the benchmark condition, (1) the P300 amplitude significantly decreased (8.92 ± 1.24 μV versus 7.84 ± 1.40 μV, p = 0.021) and latency was significantly prolonged (582.39 ± 25.02 ms versus 643.83 ± 26.16 ms, p = 0.028) only under the weakest hidden condition, and (2) the detection accuracy decreased by less than 2% (75.04 ± 3.24% versus 73.35 ± 3.15%, p = 0.029) with a more than 90% reduction in channel number (62 channels versus 6 channels), determined using the proposed channel selection method under the weakest hidden condition. Our study can provide new insights into target detection under weak hidden conditions based on EEG signals with a rapid serial visual presentation paradigm. In addition, it may expand the application of brain–computer interfaces in EEG-based target detection areas.

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Section: Discussionmentioning

confidence: 99%

EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions

Lian,

Qiao,

Zhao

et al. 2023

Brain Sciences

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“…Brain-computer interface (BCI) technology enables direct communication between humans and computers or other external devices by interpreting brain electrical activity (Cecotti and Graser, 2010 ; Manor and Geva, 2015 ). BCI technology has a wide range of applications across various domains, such as motion direction recognition (Zhang et al, 2022a ), emotion recognition (Chen et al, 2019 ; Joshi and Ghongade, 2021 ; Tao et al, 2023 ), and epileptic seizure detection (Xu et al, 2020 ; Dissanayake et al, 2021 ; Jana and Mukherjee, 2021 ; Wang B. et al, 2023 ). Concurrently, researchers are actively investigating the potential application of electroencephalography (EEG) in the realm of target recognition (Lan et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

PSAEEGNet: pyramid squeeze attention mechanism-based CNN for single-trial EEG classification in RSVP task

Yuan,

Zhou,

Wang

et al. 2024

Front. Hum. Neurosci.

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IntroductionAccurate classification of single-trial electroencephalogram (EEG) is crucial for EEG-based target image recognition in rapid serial visual presentation (RSVP) tasks. P300 is an important component of a single-trial EEG for RSVP tasks. However, single-trial EEG are usually characterized by low signal-to-noise ratio and limited sample sizes.MethodsGiven these challenges, it is necessary to optimize existing convolutional neural networks (CNNs) to improve the performance of P300 classification. The proposed CNN model called PSAEEGNet, integrates standard convolutional layers, pyramid squeeze attention (PSA) modules, and deep convolutional layers. This approach arises the extraction of temporal and spatial features of the P300 to a finer granularity level.ResultsCompared with several existing single-trial EEG classification methods for RSVP tasks, the proposed model shows significantly improved performance. The mean true positive rate for PSAEEGNet is 0.7949, and the mean area under the receiver operating characteristic curve (AUC) is 0.9341 (p < 0.05).DiscussionThese results suggest that the proposed model effectively extracts features from both temporal and spatial dimensions of P300, leading to a more accurate classification of single-trial EEG during RSVP tasks. Therefore, this model has the potential to significantly enhance the performance of target recognition systems based on EEG, contributing to the advancement and practical implementation of target recognition in this field.

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“…Recent innovations in brain recording technologies have enabled simultaneous investigation of vast neuronal populations. On one hand, this is leading to the replacement of conventional methods that rely on analyzing the activity of individual neurons with an ensemble approach, providing insights that could not be inferred solely from the modulation of single units [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. On the other hand, the broad spectrum of recording technologies and the resulting abundance of experimental data have posed challenges in developing standardized routines of analysis consistent across species and conditions.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal transformers for decoding neural movement control

Candelori,

Bardella,

Spinelli

et al. 2024

Preprint

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Deep learning tools applied to high-resolution neurophysiological data have significantly progressed, offering enhanced decoding, real-time processing, and readability for practical applications. However, the design of artificial neural networks to analyze neural activity remains a challenge, requiring a delicate balance between efficiency in low-data regimes and the interpretability of the results. To this end, we introduce a novel specialized transformer architecture to analyze single-neuron spiking activity. We test our model on multi electrodes recordings from the dorsal premotor cortex (PMd) of non-human primates while performing a motor inhibition task. The proposed architecture provides a very early prediction of the correct movement direction - no later than 230ms after the Go signal presentation across animals - and can accurately forecast whether the movement will be generated or withheld before a Stop signal, unattended, is actually presented. We also analyze the internal dynamics of the model by computing the predicted correlations between time steps and between neurons at successive layers of the architecture. We find that their evolution mirrors previous theoretical analyses. Overall, our framework provides a comprehensive use case for the practical implementation of deep learning tools in motor control research.

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Motion direction prediction through spike timing based on micro Capsnet networks

Cited by 8 publications

References 24 publications

EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions

EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions

PSAEEGNet: pyramid squeeze attention mechanism-based CNN for single-trial EEG classification in RSVP task

Spatio-temporal transformers for decoding neural movement control

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