Effect of Spatial Filtering and Channel Selection on Motor Imagery BCI

Dai, Honghui; Su, Songlin; Zhang, Ying; Jian, Wenjuan

doi:10.1145/3433996.3434046

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…An important factor affecting the efficiency of an online BCI system is the number of channels. Considering the control accuracy, MI-BCI usually requires multiple channels of EEG data, but too many channels can lead to long processing times, thus hindering its application in real-life situations [7]. In this paper, we propose a Weighted Time-domain feature extraction method.…”

Section: Introductionmentioning

confidence: 99%

Online BCI systems: cross-subject motor imagery classification based on weighted time-domain feature extraction methods

Yang,

Wang,

Peng

et al. 2024

Journal of Engineering Design

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This research project has been approved by the Ethics Committee ofZhejiang University and was conducted in strict accordance with the Declaration of Helsinki. In this study, we respected and protected the rights and privacy of the participants and ensured the confidentiality of their personal information. Participants' informed consent:We explained the purpose, process, risks, and benefits of the study to all individuals participating in the study either orally or in writing, and obtained their informed consent. Participants had the right to know that their participation was voluntary and that they could withdraw from the study at any time.

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Section: Introductionmentioning

confidence: 99%

Online BCI systems: cross-subject motor imagery classification based on weighted time-domain feature extraction methods

Yang,

Wang,

Peng

et al. 2024

Journal of Engineering Design

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“…Therefore, improving the performance of BCI systems with fewer channels is one of the future research directions to reduce the experimental preparation time and promote the portability of BCIs. In this study, we further investigate reducing calibration and improving the user-friendliness of BCI based on the previous application of CSP to few-channel motor imagery BCI (Dai et al, 2020 ), and propose an improved algorithm based on transfer learning, VFBRCSP, combining FBCSP and RCSP, and introducing EA processing. We compare the VFBRCSP on the BCI Competition IV Dataset 2a with other new methods proposed in recent years.…”

Section: Introductionmentioning

confidence: 99%

Application of multi-task transfer learning: The combination of EA and optimized subband regularized CSP to classification of 8-channel EEG signals with small dataset

Long

Wan

Jian

et al. 2023

Front. Hum. Neurosci.

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IntroductionThe volume conduction effect and high dimensional characteristics triggered by the excessive number of channels of EEG cap-acquired signals in BCI systems can increase the difficulty of classifying EEG signals and the lead time of signal acquisition. We aim to combine transfer learning to decode EEG signals in the few-channel case, improve the classification performance of the motor imagery BCI system across subject cases, reduce the cost of signal acquisition performed by the BCI system, and improve the usefulness of the system.MethodsDataset2a from BCI CompetitionIV(2008) was used as Dataset1, and our team's self-collected dataset was used as Dataset2. Dataset1 acquired EEG signals from 9 subjects using a 22-channel device with a sampling frequency of 250 Hz. Dataset2 acquired EEG signals from 10 healthy subjects (8 males and 2 females; age distribution between 21-30 years old; mean age 25 years old) using an 8-channel system with a sampling frequency of 1000 Hz. We introduced EA in the data preprocessing process to reduce the signal differences between subjects and proposed VFB-RCSP in combination with RCSP and FBCSP to optimize the effect of feature extraction.ResultsExperiments were conducted on Dataset1 with EEG data containing only 8 channels and achieved an accuracy of 78.01 and a kappa coefficient of 0.54. The accuracy exceeded most of the other methods proposed in recent years, even though the number of channels used was significantly reduced. On Dataset 2, an accuracy of 59.77 and a Kappa coefficient of 0.34 were achieved, which is a significant improvement compared to other poorly improved classical protocols.DiscussionOur work effectively improves the classification of few-channel EEG data. It overcomes the dependence of existing algorithms on the number of channels, the number of samples, and the frequency band, which is significant for reducing the complexity of BCI models and improving the user-friendliness of BCI systems.

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Online BCI systems: cross-subject motor imagery classification based on weighted time-domain feature extraction methods

Yang,

Wang,

Chen

et al. 2024

Preprint

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Motor imagery electroencephalogram (MI-EEG) is becoming increasingly important. This paper solves the problem of online signal recognition for motor imagery across subjects by finding common features across multiple subjects to improve the generality of the classification model. We analyzed the EEG data from left/right-hand motor imagery of eight subjects and proposed a weighted time-domain (WTD) feature extraction method based on a weighted channel screening method. The classification model constructed by combining this feature extraction method with the support vector machine (SVM) classification method was faster in classification and achieved good cross-subject classification accuracy (The average offline classification accuracy was 91.39%). In this paper, an online control system for asynchronous brain-controlled wheelchairs was built with good performance. The online average motor imagery classification accuracy was 81.67%, and the average response time was 1.36s. This method contributes to bringing the online Brain-computer interface (BCI) system out of the laboratory and into wider application.

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Effect of Spatial Filtering and Channel Selection on Motor Imagery BCI

Cited by 4 publications

References 13 publications

Online BCI systems: cross-subject motor imagery classification based on weighted time-domain feature extraction methods

Online BCI systems: cross-subject motor imagery classification based on weighted time-domain feature extraction methods

Application of multi-task transfer learning: The combination of EA and optimized subband regularized CSP to classification of 8-channel EEG signals with small dataset

Online BCI systems: cross-subject motor imagery classification based on weighted time-domain feature extraction methods

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