A Review on the Components of EEG-based Motor Imagery Classification with Quantitative Comparison

Rahman, M. K. M.; Joadder, Md. A. Mannan

doi:10.22496/atct20170122133

Cited by 18 publications

(8 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this study the authors proposed a novel approach but the average accuracy for the test signal is not up to the mark. Rahman et al [23] reviewed different techniques for each step of MI-based BCI and discussed detailed comparative performance analysis that can guide us to select the right technique for a particular experimental setup. This study found that important channel and feature selection are more sensitive to boost the performance of a BCI system.…”

Section: Introductionmentioning

confidence: 99%

Subject-specific EEG channel selection using non-negative matrix factorization for lower-limb motor imagery recognition

et al. 2020

View full text Add to dashboard Cite

Objective. This study aims to propose and validate a subject-specific approach to recognize two different cognitive neural states (relax and pedaling motor imagery (MI)) by selecting the relevant electroencephalogram (EEG) channels. The main aims of the proposed work are: (i) to reduce the computational complexity of the BCI systems during MI detection by selecting the relevant EEG channels, (ii) to reduce the amount of data overfitting that may arise due to unnecessary channels and redundant features, and (iii) to reduce the classification time for real-time BCI applications. Approach. The proposed method selects subject-specific EEG channels and features based on their MI. In this work, we make use of non-negative matrix factorization to extract the weight of the EEG channels based on their contribution to MI detection. Further, the neighborhood component analysis is used for subject-specific feature selection. Main results. We executed the experiments using EEG signals recorded for MI where ten healthy subjects performed MI movement of the lower limb to generate motor commands. An average accuracy of 96.66%, average true positive rate (TPR) of 97.77%, average false positives rate of 4.44%, and average Kappa of 93.33% were obtained. The proposed subject-specific EEG channel selection based MI recognition system provides 13.20% improvement in detection accuracy, and 27% improvement in Kappa value with less number of EEG channels compared to the results obtained using all EEG channels. Significance. The proposed subject-specific BCI system has been found significantly advantageous compared to the typical approach of using a fixed channel configuration. This work shows that fewer EEG channels not only reduce computational complexity and processing time (two times faster) but also improve the MI detection performance. The proposed method selects EEG locations related to the foot movement, which may be relevant for neuro-rehabilitation using lower-limb movements that may provide a real-time and more natural interface between patient and robotic device.

show abstract

Section: Introductionmentioning

confidence: 99%

Subject-specific EEG channel selection using non-negative matrix factorization for lower-limb motor imagery recognition

et al. 2020

View full text Add to dashboard Cite

show abstract

“…D k can be seen as a measure of error, the lower the better. Then, ten subjects were selected to discuss the changes in three measures under different clustering numbers (2)(3)(4)(5)(6)(7)(8)(9)(10). The results are shown in Figure 7.…”

Section: Number Of Microstate Mapsmentioning

confidence: 99%

Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis

Cui,

Xie,

et al. 2023

Brain Sciences

View full text Add to dashboard Cite

Motor imagery (MI) electroencephalography (EEG) is natural and comfortable for controllers, and has become a research hotspot in the field of the brain–computer interface (BCI). Exploring the inter-subject MI-BCI performance variation is one of the fundamental problems in MI-BCI application. EEG microstates with high spatiotemporal resolution and multichannel information can represent brain cognitive function. In this paper, four EEG microstates (MS1, MS2, MS3, MS4) were used in the analysis of the differences in the subjects’ MI-BCI performance, and the four microstate feature parameters (the mean duration, the occurrences per second, the time coverage ratio, and the transition probability) were calculated. The correlation between the resting-state EEG microstate feature parameters and the subjects’ MI-BCI performance was measured. Based on the negative correlation of the occurrence of MS1 and the positive correlation of the mean duration of MS3, a resting-state microstate predictor was proposed. Twenty-eight subjects were recruited to participate in our MI experiments to assess the performance of our resting-state microstate predictor. The experimental results show that the average area under curve (AUC) value of our resting-state microstate predictor was 0.83, and increased by 17.9% compared with the spectral entropy predictor, representing that the microstate feature parameters can better fit the subjects’ MI-BCI performance than spectral entropy predictor. Moreover, the AUC of microstate predictor is higher than that of spectral entropy predictor at both the single-session level and average level. Overall, our resting-state microstate predictor can help MI-BCI researchers better select subjects, save time, and promote MI-BCI development.

show abstract

“…The next stage is FFT. At this stage, the sample frames 𝑁 = 15.360 point in the time domain converted into the frequency domain with (5).…”

Section: 𝑁 = 𝑠𝑎𝑚𝑝𝑙𝑖𝑛𝑔 X 𝑓𝑠mentioning

confidence: 99%

“…The EEG-based system measures the bioelectrical activity of the brain and converts it into information about hand movements. The biological activity of the brain to command its limb to touch or move an object with its limb is called motor imagery [5]. EEG sensors offer a high temporal resolution, easy to transport [6], and available for monitoring the bioelectrical activity of the brain.…”

Section: Introductionmentioning

confidence: 99%

Classification of motor imagery brain wave for bionic hand movement using multilayer perceptron

Priyatno

Prakoso

Riyadi

2022

Sinergi

View full text Add to dashboard Cite

Physical disability due to amputation can affect a person's quality of life due to limited movement in performing daily activities. Bionic hands are used to help someone with an amputation disability. This research developed a bionic hand control based on electroencephalography sensors capable of measuring the brain's bioelectric activity. The classified brain wave was then translated as activity pattern information. The alpha & beta waves were the focus of this work. This study demonstrated a method to extract and classify motor imagery of brainwave activity patterns. The Fast Fourier Transform (FFT) method extracts motor imagery characteristics. The extraction of features is then classified by the Multilayer Perceptron (MLP) method for five classes of bionic hand movement. Testing was conducted with two scenarios. The first test motor imagery without additional movement showed an accuracy of 77.20 %, while the second test motor imagery combined with head movement showed an accuracy of 84.40% for five classes. The system based on motor imagery has been implemented in a bionic hand that shows the applicability of the proposed method.

show abstract

A Review on the Components of EEG-based Motor Imagery Classification with Quantitative Comparison

Cited by 18 publications

References 71 publications

Subject-specific EEG channel selection using non-negative matrix factorization for lower-limb motor imagery recognition

Subject-specific EEG channel selection using non-negative matrix factorization for lower-limb motor imagery recognition

Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis

Classification of motor imagery brain wave for bionic hand movement using multilayer perceptron

Contact Info

Product

Resources

About