Research on the Key Technologies of Motor Imagery EEG Signal Based on Deep Learning

Wang, Zhuozheng; Ma, Zhuo; Du, Xiuwen; Dong, Yuan; Liu, Wei

doi:10.32629/jai.v2i4.60

Cited by 3 publications

(4 citation statements)

References 15 publications

(11 reference statements)

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“…Traditionally, MI-BCIs operate on machine learning (ML) algorithms in which spatial features associated with movement imagination are recognized. The imagining of a left or right body movement is accompanied by a lateralization of event-related (de)synchronization (ERD/ERS) in the mu (7-13 Hz) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) frequency bands of EEG signals [7][8][9][10]. This brain activity feature is usually picked up by the Common Spatial Pattern (CSP) algorithm [11] and serves as an input to the ML algorithm classifying the imagined body movements.…”

Section: Introductionmentioning

confidence: 99%

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Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users

Leeuwis¹,

Alimardani²

2022

PLoS ONE

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Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with mental imagination of movement and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine Learning (ML) algorithms, which require extensive signal processing and feature engineering to extract changes in sensorimotor rhythms (SMR). In recent years, Deep Learning (DL) models have gained popularity for EEG classification as they provide a solution for automatic extraction of spatio-temporal features in the signals. However, past BCI studies that employed DL models, only attempted them with a small group of participants, without investigating the effectiveness of this approach for different user groups such as inefficient users. BCI inefficiency is a known and unsolved problem within BCI literature, generally defined as the inability of the user to produce the desired SMR patterns for the BCI classifier. In this study, we evaluated the effectiveness of DL models in capturing MI features particularly in the inefficient users. EEG signals from 54 subjects who performed a MI task of left- or right-hand grasp were recorded to compare the performance of two classification approaches; a ML approach vs. a DL approach. In the ML approach, Common Spatial Patterns (CSP) was used for feature extraction and then Linear Discriminant Analysis (LDA) model was employed for binary classification of the MI task. In the DL approach, a Convolutional Neural Network (CNN) model was constructed on the raw EEG signals. Additionally, subjects were divided into high vs. low performers based on their online BCI accuracy and the difference between the two classifiers’ performance was compared between groups. Our results showed that the CNN model improved the classification accuracy for all subjects within the range of 2.37 to 28.28%, but more importantly, this improvement was significantly larger for low performers. Our findings show promise for employment of DL models on raw EEG signals in future MI-BCI systems, particularly for BCI inefficient users who are unable to produce desired sensorimotor patterns for conventional ML approaches.

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Section: Introductionmentioning

confidence: 99%

“…DL generates high-level abstract features from low-level features by identifying distributed patterns in the acquired data. Hence, DL models hold the potential of handling complex and non-linear high dimensional data [10].…”

Section: Introductionmentioning

confidence: 99%

Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users

Leeuwis¹,

Alimardani²

2022

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Traditionally, MI-BCIs operate on machine learning (ML) algorithms in which spatial features associated with movement imagination are recognized. The imagining of a left or right body movement is accompanied by a lateralization of event-related (de)synchronization (ERD/ERS) in the mu (7-13 Hz) and beta (13-30 Hz) frequency bands of EEG signals (Pfurtscheller et al, 2006; Avanzini et al, 2012; Barros & Neto, 2018; Wang et al, 2019). This brain activity feature serves as an input to the ML algorithm classifying the imagined body movements.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Promise of Deep Learning for BCIs: Classification of Motor Imagery EEG using Convolutional Neural Network

Leeuwis

Alimardani

2021

Preprint

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Motor Imagery (MI) is a mental process by which an individual rehearses body movements without actually performing physical actions. Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with this mental process and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine Learning (ML) algorithms, which require extensive signal processing and feature engineering to extract changes in sensorimotor rhythms (SMR). However, in recent years, Deep Learning (DL) models have gained popularity for EEG classification as they provide a solution for automatic extraction of spatio-temporal features in the signals. In this study, EEG signals from 54 subjects who performed a MI task of left- or right-hand grasp was employed to compare the performance of two MI-BCI classifiers; a ML approach vs. a DL approach. In the ML approach, Common Spatial Patterns (CSP) was used for feature extraction and then Linear Discriminant Analysis (LDA) model was employed for binary classification of the MI task. In the DL approach, a Convolutional Neural Network (CNN) model was constructed on the raw EEG signals. The mean classification accuracies achieved by the CNN and CSP+LDA models were 69.42% and 52.56%, respectively. Further analysis showed that the DL approach improved the classification accuracy for all subjects within the range of 2.37 to 28.28% and that the improvement was significantly stronger for low performers. Our findings show promise for employment of DL models in future MI-BCI systems, particularly for BCI inefficient users who are unable to produce desired sensorimotor patterns for conventional ML approaches.

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Analysis of the EEG Signal of the League Member Awareness of College Members Based on the IoT Measurement

Zheng

2021

2021 Fifth International Conference on I-Smac (IoT in Social, Mobile, Analytics and Cloud) (I-Smac)

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Research on the Key Technologies of Motor Imagery EEG Signal Based on Deep Learning

Cited by 3 publications

References 15 publications

Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users

Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users

The Promise of Deep Learning for BCIs: Classification of Motor Imagery EEG using Convolutional Neural Network

Analysis of the EEG Signal of the League Member Awareness of College Members Based on the IoT Measurement

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