EEG Signals Classification: Motor Imagery for Driving an Intelligent Wheelchair

Pinheiro, Oberdan Rocha; Alves, Lynn; Souza, Josemar Rodrigues de

doi:10.1109/tla.2018.8291481

Cited by 43 publications

(17 citation statements)

References 14 publications

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“…The application of BCI in rehabilitation training can help normal thinking patients with paralysis of the neuromuscular system interact with the outside world (Leeb et al, 2015 ; Rupp et al, 2015 ; Müller-Putz et al, 2017 ; Wang L. et al, 2019 ). In addition, EEG studies were conducted on the control of an intelligent wheelchair (Zhang et al, 2016 ; Pinheiro et al, 2018 ), robotic arm (Meng et al, 2016 ), and other external devices (He et al, 2015 ; Edelman et al, 2019 ). A major challenge of the BCI is to interpret movement intention from brain activity.…”

Section: Introductionmentioning

confidence: 99%

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

Lun

Chen

et al. 2020

Front. Hum. Neurosci.

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A brain-computer interface (BCI) based on electroencephalography (EEG) can provide independent information exchange and control channels for the brain and the outside world. However, EEG signals come from multiple electrodes, the data of which can generate multiple features. How to select electrodes and features to improve classification performance has become an urgent problem to be solved. This paper proposes a deep convolutional neural network (CNN) structure with separated temporal and spatial filters, which selects the raw EEG signals of the electrode pairs over the motor cortex region as hybrid samples without any preprocessing or artificial feature extraction operations. In the proposed structure, a 5-layer CNN has been applied to learn EEG features, a 4-layer max pooling has been used to reduce dimensionality, and a fully-connected (FC) layer has been utilized for classification. Dropout and batch normalization are also employed to reduce the risk of overfitting. In the experiment, the 4 s EEG data of 10, 20, 60, and 100 subjects from the Physionet database are used as the data source, and the motor imaginations (MI) tasks are divided into four types: left fist, right fist, both fists, and both feet. The results indicate that the global averaged accuracy on group-level classification can reach 97.28%, the area under the receiver operating characteristic (ROC) curve stands out at 0.997, and the electrode pair with the highest accuracy on 10 subjects dataset is FC3-FC4, with 98.61%. The research results also show that this CNN classification method with minimal (2) electrode can obtain high accuracy, which is an advantage over other methods on the same database. This proposed approach provides a new idea for simplifying the design of BCI systems, and accelerates the process of clinical application.

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Section: Introductionmentioning

confidence: 99%

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

Lun

Chen

et al. 2020

Front. Hum. Neurosci.

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“…It was found that the error rate using K-means clustering combined with BP network is much smaller than the latter. It is also found that K-means plus LVQ is also a very useful feature identifier in EEG classification problems [14]. By analyzing the EEG signals in the brain regions of the left and right hands, Pfurtscheller is able to distinguish between EEGs with left and right hand movements with an accuracy of 85% [15].…”

Section: Introductionmentioning

confidence: 99%

Feature recognition of motor imaging EEG signals based on deep learning

Shi

Ren

Cui

2019

Pers Ubiquit Comput

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The brain-computer interface technology interprets the EEG signals displayed by the human brain's neurological thinking activities through computers and instruments, and directly uses the interpreted information to manipulate the outside world, thereby abandoning the human peripheral nerves and muscle systems. The emergence of brain-computer interface technology has brought practical value to many fields. Based on the mechanism and characteristics of motion imaging EEG signals, this paper designs the acquisition experiment of EEG signals. After removing the anomalous samples, the wavelet-reconstruction method is used to extract the specific frequency band of the motion imaging EEG signal. According to the characteristics of motor imagery EEG signals, the feature recognition algorithm of convolutional neural networks is discussed. After an in-depth analysis of the reasons for choosing this algorithm, a variety of different network structures were designed and trained. The optimal network structure is selected by analyzing the experimental results, and the reasons why the structure effect is superior to other structures are analyzed. The results show that the method has a high accuracy rate for the recognition of motor imagery EEG, and it has good robustness.

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“…MI can be divided into simple imagery, which involves a single part of limb movements, and compound imagery, which involves not less than two parts of limb movements [ 5 , 6 ]. With the advancement of brain science in recent years, MI training has gradually become a new rehabilitation method for patients with limb motor dysfunction caused by brain injury [ 7 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network

et al. 2021

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Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain-computer interface. To improve classification accuracy, we propose a novel feature extraction method in which the connectivity increment rate (CIR) of the brain function network (BFN) is extracted. First, the BFN is constructed on the basis of the threshold matrix of the Pearson correlation coefficient of the mu rhythm among the channels. In addition, a weighted BFN is constructed and expressed by the sum of the existing edge weights to characterize the cerebral cortex activation degree in different movement patterns. Then, on the basis of the topological structures of seven mental tasks, three regional networks centered on the C3, C4, and Cz channels are constructed, which are consistent with correspondence between limb movement patterns and cerebral cortex in neurophysiology. Furthermore, the CIR of each regional functional network is calculated to form three-dimensional vectors. Finally, we use the support vector machine to learn a classifier for multiclass MI tasks. Experimental results show a significant improvement and demonstrate the success of the extracted feature CIR in dealing with MI classification. Specifically, the average classification performance reaches 88.67% which is higher than other competing methods, indicating that the extracted CIR is effective for MI classification.

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EEG Signals Classification: Motor Imagery for Driving an Intelligent Wheelchair

Cited by 43 publications

References 14 publications

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals

Feature recognition of motor imaging EEG signals based on deep learning

Research on Recognition of Motor Imagination Based on Connectivity Features of Brain Functional Network

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