Extracting Motor Imagery Features to Control Two Robotic Hands

Reust, Adam; Desai, Jaydip; Gomez, Louis M.

doi:10.1109/isspit.2018.8642627

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…It was demonstrated in several research [38,39] that the MI-EEG signals from the C3, C4 and Cz electrodes (G-I) can obviously demonstrate the ERS/ERD characteristics. Thus, we will discuss the classification results from various fusion grouped of electrodes including G-I.…”

Section: Groupmentioning

confidence: 99%

A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

Yang

Wang

et al. 2020

IEEE Access

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Motor imagery electroencephalography (MI-EEG), which is an important subfield of active brain-computer interface (BCI) systems, can be applied to help disabled people to consciously and directly control prosthesis or external devices, aiding them in certain daily activities. However, the low signal-tonoise ratio and spatial resolution make MI-EEG decoding a challenging task. Recently, some deep neural approaches have shown good improvements over state-of-the-art BCI methods. In this study, an end-to-end scheme that includes a multi-layer convolution neural network is constructed for an accurate spatial representation of multi-channel grouped MI-EEG signals, which is employed to extract the useful information present in a multi-channel MI signal. Then the invariant spatial representations are captured from across-subjects training for enhancing the generalization capability through a stacked sparse autoencoder framework, which is inspired by representative deep learning models. Furthermore, a quantitative experimental analysis is conducted on our private dataset and on a public BCI competition dataset. The results show the effectiveness and significance of the proposed methodology.INDEX TERMS brain-computer interface, discriminative and representative deep learning, feature fusion, convolution neural network, stacked sparse autoencoder

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Section: Groupmentioning

confidence: 99%

A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

Yang

Wang

et al. 2020

IEEE Access

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“…Motor imagery (MI)-based BCI is an important spontaneous BCI system that has been extensively investigated. Reust and his colleagues employed an MI-BCI system corresponding to human hand movement to control two robotic hands; this approach achieved a 95% classification accuracy overall (Reust et al, 2018 ). Another novel mental imagery system developed by the University of Montreal employed a multimodal BCI system to control the single-step and forward walking status using an immersive virtual reality avatar (Alchalabi et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm

Liu

et al. 2022

Front. Neurosci.

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Multiple types of brain-control systems have been applied in the field of rehabilitation. As an alternative scheme for balancing user fatigue and the classification accuracy of brain–computer interface (BCI) systems, facial-expression-based brain control technologies have been proposed in the form of novel BCI systems. Unfortunately, existing machine learning algorithms fail to identify the most relevant features of electroencephalogram signals, which further limits the performance of the classifiers. To address this problem, an improved classification method is proposed for facial-expression-based BCI (FE-BCI) systems, using a convolutional neural network (CNN) combined with a genetic algorithm (GA). The CNN was applied to extract features and classify them. The GA was used for hyperparameter selection to extract the most relevant parameters for classification. To validate the superiority of the proposed algorithm used in this study, various experimental performance results were systematically evaluated, and a trained CNN-GA model was constructed to control an intelligent car in real time. The average accuracy across all subjects was 89.21 ± 3.79%, and the highest accuracy was 97.71 ± 2.07%. The superior performance of the proposed algorithm was demonstrated through offline and online experiments. The experimental results demonstrate that our improved FE-BCI system outperforms the traditional methods.

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“…An innovator solution able to provide an alternative way of regaining their independence and confidence is the Brain-Computer Interface (BCI). BCI is a thought-provoking field with a rapid evolution because of its applications based on brain-controlled mechatronics devices (wheelchairs [5][6][7][8], robot arm [9][10], robot hand [11], mobile robots [12], household items [13] and intelligent home [14]) or mind-controlled virtual keyboards [15] or 3D simulations. The working principle underlying a Brain-Computer Interface is consisting of the following main phases: acquisition [16], processing, features extraction [17], and classification of signals related to brain patterns [18] triggered by the execution of specific cognitive tasks [19], [20], followed by the translation of the detected biopotentials and transmission of commands to the controlled applications.…”

Section: Introductionmentioning

confidence: 99%

A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

Rușanu¹

2022

Preprint

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This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

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Extracting Motor Imagery Features to Control Two Robotic Hands

Cited by 11 publications

References 9 publications

A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm

A LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

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