A New Motor Imagery EEG Classification Method FB-TRCSP+RF Based on CSP and Random Forest

Zhang, Ranran; Xiao, Xiaoyan; Li, Zhi; Jiang, Wei; Li, Jianwen; Cao, Yankun; Ren, Jianmin; Jiang, Dongmei; Cui, Lizhen

doi:10.1109/access.2018.2860633

Cited by 24 publications

(11 citation statements)

References 17 publications

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“…In the part of classification, plenty of traditional machine learning methods were proposed to recognize MI on features from EEG signals. They include support vector machine (SVM) [19], Random Forest [20], Bayesian classifier, linear discriminant analysis (LDA) [23], etc. They were simple and useful, but there is still being a challenge to improve the classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

MLP With Riemannian Covariance for Motor Imagery Based EEG Analysis

et al. 2020

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Stroke is one of the leading causes of disability and incidence. For the treatment of prognosis of stroke patients, Motor imagery (MI) as a novel experimental paradigm, clinically it is effective because MI based Brain-Computer interface system can promote rehabilitation of stroke patients. There is being a hot and challenging topic to recognize multi-class motor imagery action classification accurately based on electroencephalograph (EEG) signals. In this work, we propose a novel framework named MRC-MLP. Multiple Riemannian covariance is used for EEG feature extraction. We make a multi-scale spectral division to filter EEG signals. They consist of different frequency bandwidths name sub-band. We concatenate and vectorize features extracted by Riemannian covariance on each sub-band. We design a fully connected MLP model with an improved loss function for motor imagery EEG classification. Furthermore, our proposed method MRC-MLP outperforms state-of-the-art methods and achieves approximately mean accuracy with 76%.

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Section: Related Workmentioning

confidence: 99%

MLP With Riemannian Covariance for Motor Imagery Based EEG Analysis

et al. 2020

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“…The ANN has a high demand for computing power and a long training time, so it is not suitable for the scene with real-time requirements. A random forest algorithm for EEG signal classification was exhibited in [ 21 ], and the accuracy reaches 89.9% after combining the common spatial pattern (CSP). The random forest algorithm is not ideal for data with few feature dimensions, so it is not applicable in the pressure signal classification of this article individually.…”

Section: Introductionmentioning

confidence: 99%

Plantar Pressure Detection System Based on Flexible Hydrogel Sensor Array and WT-RF

Liu

Xiao

Wang

et al. 2021

Sensors

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This paper presents a hydrogel-based flexible sensor array to detect plantar pressure distribution and recognize the gait patterns to assist those who suffer from gait disorders to rehabilitate better. The traditional pressure detection array is composed of rigid metal sensors, which have poor biocompatibility and expensive manufacturing costs. To solve the above problems, we have designed and fabricated a novel flexible sensor array based on AAM/NaCl (Acrylamide/Sodium chloride) hydrogel and PI (Polyimide) membrane. The proposed array exhibits excellent structural flexibility (209 KPa) and high sensitivity (12.3 mV·N−1), which allows it to be in full contact with the sole of the foot to collect pressure signals accurately. The Wavelet Transform-Random Forest (WT-RF) algorithm is introduced to recognize the gaits based on the plantar pressure signals. Wavelet transform realizes the signal filtering and normalization, and random forest is responsible for the classification of the processed signals. The classification accuracy of the WT-RF algorithm reaches 91.9%, which ensures the precise recognition of gaits.

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“…Especially, the common spatial pattern (CSP) approach [4]- [7] has been used successfully in MI classi-fication by extracting ERD/ERS-related features. Recently, the various extensions of the CSP which overcome a frequency band dependency problem have been proposed such as filter-bank CSP (FBCSP) [8], sub-band regularized CSP (SBRCSP) [9], filter-bank regularized CSP (FBRCSP) [10], sparse filter band common spatial pattern (SFBCSP) [11], and filter band combined with Tikhonov regularization CSP (FB-TRCSP) [12]. Moreover, the temporally constrained sparse group spatial pattern (TSGCSP) [13] and sparse group representation model (SGRM) [14] is proposed to overcome a time period dependency and subject-dependency problem, respectively and shows improved performance for MI-classification.…”

Section: Introductionmentioning

confidence: 99%

Optimal Channel Selection Using Correlation Coefficient for CSP Based EEG Classification

Park

Chung

2020

IEEE Access

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In this paper, we present an optimal channel selection method to improve common spatial pattern (CSP) related features for motor imagery (MI) classification. In contrast to existing channel selection methods, in which channels significantly contributing to the classification in terms of the signal power are selected, distinctive channels in terms of correlation coefficient values are selected in the proposed method. The distinctiveness of a channel is quantified by the number of channels with which it yields large difference in correlation coefficient values for binary motor imagery (MI) tasks, rather than by the largeness of the difference itself. For each distinctive channel, a group of channels is formed by gathering strongly correlated channels and the Fisher score is computed using the feature output, based on the filter-bank CSP (FBCSP) exclusively applied to the channel group. Finally, the channel group with the highest Fisher score is chosen as the selected channels. The proposed method selects the fewest channels on average and outperforms existing channel selection approaches. The simulation results confirm performance improvement for two publicly available BCI datasets, BCI competition III dataset IVa and BCI competition IV dataset I, in comparison with existing methods. INDEX TERMS electroencephalography (EEG), Brain-Computer Interfaces (BCIs), correlation coefficient, common spatial pattern (CSP), channel selection

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A New Motor Imagery EEG Classification Method FB-TRCSP+RF Based on CSP and Random Forest

Cited by 24 publications

References 17 publications

MLP With Riemannian Covariance for Motor Imagery Based EEG Analysis

MLP With Riemannian Covariance for Motor Imagery Based EEG Analysis

Plantar Pressure Detection System Based on Flexible Hydrogel Sensor Array and WT-RF

Optimal Channel Selection Using Correlation Coefficient for CSP Based EEG Classification

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