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A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics
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作者 sen tian Jin Zhang +3 位作者 Xuanyu Shu Lingyu Chen Xin Niu You Wang 《Journal of Bionic Engineering》 SCIE EI CSCD 2022年第1期224-239,共16页
With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improving towards diversification and intelligence.However,the model is more evaluated from the pros and con... With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improving towards diversification and intelligence.However,the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive enough.Hence,a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the paper.Firstly,four classical neural network models are illustrated:Back Propagation(BP)network,Deep Belief Network(DBN),LeNet5 network,and olfactory bionic model(KIII model),and the neuron transmission mode and equation,network structure,and weight updating principle of the models are analyzed qualitatively.The analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models,and the LeNet5 network simulates the nervous system in depth.Secondly,evaluation indexes of ANN are constructed from the perspective of bionics in this paper:small-world,synchronous,and chaotic characteristics.Finally,the network model is quantitatively analyzed by evaluation indexes from the perspective of bionics.The experimental results show that the DBN network,LeNet5 network,and BP network have synchronous characteristics.And the DBN network and LeNet5 network have certain chaotic characteristics,but there is still a certain distance between the three classical neural networks and actual biological neural networks.The KIII model has certain small-world characteristics in structure,and its network also exhibits synchronization characteristics and chaotic characteristics.Compared with the DBN network,LeNet5 network,and the BP network,the KIII model is closer to the real biological neural network. 展开更多
关键词 Artificial neural network(ANN) Back Propagation(BP)network Deep Belief Network(DBN) LeNet5 network Olfactory bionic model(KIII model) Small world Chaos SYNCHRONOUS
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A large database towards user-friendly SSVEP-based BCI
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作者 Yue Dong sen tian 《Brain Science Advances》 2023年第4期297-309,共13页
Background: Brain-computer interfaces(BCIs) have gained considerable attention for their potential in assisting individuals who have motor impairments with communication and rehabilitation.Among BCIs, steady-state vis... Background: Brain-computer interfaces(BCIs) have gained considerable attention for their potential in assisting individuals who have motor impairments with communication and rehabilitation.Among BCIs, steady-state visual evoked potential(SSVEP)-based systems have demonstrated high efficiency in interactive applications.However, ergonomic design challenges have limited their practical implementation in industrial settings. Issues such as visual and mental fatigue caused by flickering stimuli and the time-consuming preparation process hinder user adoption of such systems. Methods: To evaluate these BCI solutions, we introduced an open database comprising Electroencephalogram(EEG) data collected from 59 healthy volunteers using ergonomically designed semi-dry electrodes and grid stimuli. The database was acquired without electromagnetic shielding, and the preparation time for each participant was <5 min. A 40-target SSVEP speller system with cues was used in the experiment. Results: We validate the database by temporal and spectral analyzing methods. To further investigate the database, filter bank canonical correlation analysis(FBCCA), ensemble task-related component analysis(e-TRCA) and multi-stimulus task-related component analysis(msTRCA) were used for classification. The database can be downloaded from the following link: https://drive.google.com/drive/folders/1TXuxU863nZoniZRgNWZy0PRuL8lhBuP4?usp=sharing. Conclusions: This research contributes to enhancing the use of SSVEP-based BCIs in practical settings by addressing user experience and system design challenges. The proposed user-friendly visual stimuli and ergonomic electrode design improve comfort and usability. The open dataset serves as a valuable resource for future studies, enabling the development of robust and efficient SSVEP-BCI systems suitable for industrial applications. 展开更多
关键词 steady-state visual evoked potential brain-computer interface hydrogel electrode grid stimulus industrial-like setting
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