Recognizing single-trial motor imagery EEG based on interpretable clustering method

Fu, Rongrong; Li, Weishuai; Chen, Junxiang; Han, Mengmeng

doi:10.1016/j.bspc.2020.102171

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…The low-density assumption thinks that the decision boundary should go through the sparse low-density feature space. Both the cluster assumption and the low-density assumption focus on the whole data distribution by adjusting the decision boundary ( Fu et al, 2021 ). However, the smoothness assumption pays more attention to the local data distribution.…”

Section: Semi-supervised Learningmentioning

confidence: 99%

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A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain–Computer Interface

Huang

Hua

et al. 2021

Front. Neurosci.

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In an electroencephalogram- (EEG-) based brain–computer interface (BCI), a subject can directly communicate with an electronic device using his EEG signals in a safe and convenient way. However, the sensitivity to noise/artifact and the non-stationarity of EEG signals result in high inter-subject/session variability. Therefore, each subject usually spends long and tedious calibration time in building a subject-specific classifier. To solve this problem, we review existing signal processing approaches, including transfer learning (TL), semi-supervised learning (SSL), and a combination of TL and SSL. Cross-subject TL can transfer amounts of labeled samples from different source subjects for the target subject. Moreover, Cross-session/task/device TL can reduce the calibration time of the subject for the target session, task, or device by importing the labeled samples from the source sessions, tasks, or devices. SSL simultaneously utilizes the labeled and unlabeled samples from the target subject. The combination of TL and SSL can take advantage of each other. For each kind of signal processing approaches, we introduce their concepts and representative methods. The experimental results show that TL, SSL, and their combination can obtain good classification performance by effectively utilizing the samples available. In the end, we draw a conclusion and point to research directions in the future.

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Section: Semi-supervised Learningmentioning

confidence: 99%

“…Based on the cluster assumption, Fu et al (2021) presented a semi-supervised discriminative rectangle mixture approach in MI-based BCI. It is assumed that the prior distribution of decision boundaries is a Gaussian mixture model.…”

Section: Semi-supervised Learningmentioning

confidence: 99%

A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain–Computer Interface

Huang

Hua

et al. 2021

Front. Neurosci.

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“…Fu, Rongrong Et al. [14] This work investigates a methodology for single-preliminary MI EEG classification in interpretable bunching. The tensor organized EEG information under Mu beat is first handled by CSSD to acquire the multi-dimensional CSSD-planned EEG.…”

Section: Related Workmentioning

confidence: 99%

“…Despite the large utilization of CSP feature extractor, the selection of features band in EEG signals is still a challenge. Most of authors work in direction of frequency band selection, which is mainly categorized into four section, CSP combined with time-frequency analysis techniques, orthogonal empirical mode decomposition (OEMD) with FIR filter [14,15]. Many authors applied various hybrid methods of feature extraction such as combination Copyright © Authors ISSN (Print): 2204-0595 ISSN (Online): 2203-1731 of CSP and wavelet.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Motor Imagery EEG Classification Based on Feature Extraction and Machine Learning Algorithm

Chintamani¹

2021

ITII

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The brain-computer interface provides the excellent potential to address nervous system-related activity. The function of the nervous system work between internal brain control and external human body physical structure. Some parts of the human body cannot generate the signal for the processing of the human brain, cannot recognize and identify human body parts' activity—the motor imagery EEG classification approach helps resolve such types of critical illness cause of death. The dimension and structure of motor imagery-based EEG data are very high and unsupported behaviors. The machine learning and another classification algorithm cannot handle these variants of EEG data directly. For the process of better classification of motor imagery, EEG needs transformation and extraction. The transform-based feature extraction process such as DCT, DWT, SFTF and some other harmonic frequency-based applied. In this paper presents the details analysis of feature extraction and classification algorithms for motor imagery EEG classification. The machine learning provides three types of an algorithm for classification, supervised, unsupervised and semi-supervised. This paper mainly focuses on the supervised machine learning algorithm. For the analysis of machine learning algorithm use BC competition-IV dataset. MATLAB software is used as a tool for the code of algorithms and measures standard parameters such as accuracy, sensitivity and specificity.

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“…Each spatial model describes the distribution pattern of specific signals which are located in different regions of the brain. There is a great deal of research value on the synergy work mechanism of multiple brain regions [ 16 ]. Regularization factor was introduced based on the CSSD algorithm in [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive EEG Classification Algorithm Based on CSSD and ELM_Kernel for Small Training Samples

Wang

Lan

Wang

et al. 2022

Journal of Healthcare Engineering

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Rehabilitation technologies based on brain-computer interface (BCI) have become a promising approach for patients with dyskinesia to regain movement. In BCI experiment, there is often a necessary stage of calibration measurement before the feedback applications. To reduce the time required for initial training, it is of great importance to have a method which can learn to classify electroencephalogram (EEG) signals with a little amount of training data. In this paper, the novel combination of feature extraction and classification algorithm is proposed for classification of EEG signals with a small number of training samples. For feature extraction, the motor imagery EEG signals are pre-processed, and a relative distance criterion is defined to select the optimal combination of channels. Subsequently, common spatial subspace decomposition (CSSD) algorithm and extreme learning machine with kernel (ELM_Kernel) algorithm are used to perform the types of tasks classification of motor imagery EEG signals. Simulation results demonstrate that the proposed method produces a high average classification accuracy of 99.1% on BCI Competition III dataset IVa and 76.92% on BCI Competition IV dataset IIa outperforming state-of-the-art algorithms and obtains a good classification accuracy.

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Recognizing single-trial motor imagery EEG based on interpretable clustering method

Cited by 7 publications

References 27 publications

A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain–Computer Interface

A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain–Computer Interface

Analysis of Motor Imagery EEG Classification Based on Feature Extraction and Machine Learning Algorithm

An Adaptive EEG Classification Algorithm Based on CSSD and ELM_Kernel for Small Training Samples

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