Thoughts on neurophysiological signal analysis and classification

Li, Junhua

doi:10.26599/bsa.2020.9050020

Cited by 9 publications

(3 citation statements)

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“…The study of EEG classification problems is important for the application and development of the BCI system ( Hu et al, 2019 ; Hu and Zhang, 2020 ; Li, 2020 ; Li et al, 2020 ). In EEG, event-related potentials (ERPs) are specific voltage signals generated in the brain in response to a task (e.g., gazing at numbers, letters, or pictures).…”

Section: Introductionmentioning

confidence: 99%

A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network

Song

et al. 2022

Front. Comput. Neurosci.

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BackgroundElectroencephalogram (EEG)-based brain-computer interface (BCI) systems are widely utilized in various fields, including health care, intelligent assistance, identity recognition, emotion recognition, and fatigue detection. P300, the main event-related potential, is the primary component detected by EEG-based BCI systems. Existing algorithms for P300 classification in EEG data usually perform well when tested in a single participant, although they exhibit significant decreases in accuracy when tested in new participants. We attempted to address this lack of generalizability associated with existing classification methods using a novel convolutional neural network (CNN) model developed using logistic regression (LR).Materials and MethodsWe proposed an LR-CNN model comprising two parts: a combined LR-based memory model and a CNN-based generalization model. The LR-based memory model can learn the individual features of participants and addresses the decrease in accuracy caused by individual differences when applied to new participants. The CNN-based generalization model can learn the common features among participants, thereby reducing overall classification bias and improving overall classification accuracy.ResultsWe compared our method with existing, commonly used classification methods through three different sets of experiments. The experimental results indicated that our method could learn individual differences among participants. Compared with other commonly used classification methods, our method yielded a marked improvement (>90%) in classification among new participants.ConclusionThe accuracy of the proposed model in the face of new participants is better than that of existing, commonly used classification methods. Such improvements in cross-subject test accuracy will aid in the development of BCI systems.

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Section: Introductionmentioning

confidence: 99%

A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network

Song

et al. 2022

Front. Comput. Neurosci.

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“…With the development of higher order data processing, tensor analysis has drawn lots of attention and shown advantage to vectorization methods. As the most important tool in tensor analysis, tensor decomposition methods are widely applied in multi fields such as computer vision, [1][2][3] machine learning, 4,5 neuroscience, 6,7 data mining, 8,9 social network computing 10 and so on. There are several tensor decomposition methods, among which Tucker decomposition 11 is one of most famous ones.…”

Section: Introductionmentioning

confidence: 99%

Parallel tensor decomposition with distributed memory based on hierarchical singular value decomposition

Fang

Dong

et al. 2021

Concurrency and Computation

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As an important tool of multiway/tensor data analysis tool, Tucker decomposition has been applied widely in various fields. But traditional sequential Tucker algorithms have been outdated because tensor data is growing rapidly in term of size. To address this problem, in this article, we focus on parallel Tucker decomposition of dense tensors on distributed-memory systems. The proposed method uses hierarchical SVD to accelerate the SVD step in traditional sequential algorithms, which usually takes up most computation time. The data distribution strategy is designed to follow the implementation of hierarchical SVD. We also find that compared with the state-of-the-art method, the proposed method has lower communication cost in large-scale parallel cases under the assumption of the α-β model.

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“…The results could be generated once there is enough information to do so even before reaching the last layer. For more thoughts beyond deep learning please refer to Li (2020).…”

mentioning

confidence: 99%

Editorial: Recent Developments of Deep Learning in Analyzing, Decoding, and Understanding Neuroimaging Signals

2021

Front. Neurosci.

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Thoughts on neurophysiological signal analysis and classification

Cited by 9 publications

References 55 publications

A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network

A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network

Parallel tensor decomposition with distributed memory based on hierarchical singular value decomposition

Editorial: Recent Developments of Deep Learning in Analyzing, Decoding, and Understanding Neuroimaging Signals

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