Motor Imagery Data Classification for BCI Application Using Wavelet Packet Feature Extraction

Hettiarachchi, Imali; Nguyen, Thanh Thi; Nahavandi, Saeid

doi:10.1007/978-3-319-12643-2_63

Cited by 20 publications

(10 citation statements)

References 6 publications

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“…3 out of 4 participants succeeded in controlling the falling ball to an exact color basket by using two optimized MI from left hand MI, right hand MI and feet MI. One study in 2010 revealed the feasibility of using this type of game for stroke patients with a moderate classification accuracy of 60%-75% among five novices BCI subjects [2]. This result is similar to a previous study [38] with six healthy users (average classification accuracy of 69.2%).…”

Section: Action Game Genresupporting

confidence: 79%

“…Neurological disease is accepted as one of the reasons causing disability. For instance, stroke attacks more than 20 million people each year and causes 45% of the total to become permanent upper-body disabled [2], without mentioning a significant number suffer from lower limb disability or temporary limited mobility. Although there have been several advanced therapies for neurological rehabilitation, the range of compatible users and the rehabilitation efficiency are still limited.…”

Section: Introductionmentioning

confidence: 99%

“…The interaction actively navigated by a human is called active BCI mode. This novel control strategy, combined with motor imagery [2,12,13], would widen the range of potential beneficiaries of assistive or rehabilitative robotics, such as diagnosed lockin patients. For example, using MI-BCI for exoskeleton navigation would help lock-in patients to have accessibility for walking assistance by mere imagination [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving the Brain-Computer Interface Learning Process with Gamification in Motor Imagery: A Review

Gao¹,

Xia²,

Zhang³

et al. 2023

Gamification - Analysis, Design, Development and Ludification

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Brain-computer-interface-based motor imagery (MI-BCI), a control method for transferring the imagination of motor behavior to computer-based commands, could positively impact neural functions. With the safety guaranteed by non-invasive BCI devices, this method has the potential to enhance rehabilitation and physical outcomes. Therefore, this MI-BCI control strategy has been highly researched. However, applying a non-invasive MI-BCI to real life is still not ideal. One of the main reasons is the monotonous training procedure. Although researchers have reviewed optimized signal processing methods, no suggestion is found in training feedback design. The authors believe that enhancing the engagement interface via gamification presents a potential method that could increase the MI-BCI outcome. After analyzing 2524 articles (from 2001 to 2020), 28 pieces of research are finally used to evaluate the feasibility of using gamified MI-BCI system for training. This paper claims that gamification is feasible for MI-BCI training with an average accuracy of 74.35% among 111 individuals and positive reports from 26 out of 28 studies. Furthermore, this literature review suggests more emphasis should be on immersive and humanoid design for a gaming system, which could support relieving distraction, stimulate correct MI and improve learning outcomes. Interruptive training issues such as disturbing graphical interface design and potential solutions have also been presented for further research.

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Section: Action Game Genresupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving the Brain-Computer Interface Learning Process with Gamification in Motor Imagery: A Review

Gao¹,

Xia²,

Zhang³

et al. 2023

Gamification - Analysis, Design, Development and Ludification

View full text Add to dashboard Cite

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“…At present, there are several methods of MI-EEG feature extraction: autoregression model (AR) [3], wavelet packet transformation (WPT) [4], discrete wavelet transform (DWT) [5], common spatial patterns (CSP) [6], power spectral density (PSD) [7], and principal component analysis (PCA) [8]. AR, the parameters change with the input of each sample point, better reflecting the state of the brain, and it requires less computation and does not require prior knowledge of the relevant frequency band, but it is not good for nonstationary signals.…”

Section: Introductionmentioning

confidence: 99%

A Motor Imagery EEG Feature Extraction Method Based on Energy Principal Component Analysis and Deep Belief Networks

Cheng

Yu³

et al. 2020

IEEE Access

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The motor imagery electroencephalography (MI-EEG) reflects the subjective motor intention, which has received increasing attention in rehabilitation. How to extract the features of MI-EEG accurately and quickly is the key to its successful application. Based on the analysis and comparison of the existing feature extraction algorithms, a feature extraction method based on principal component analysis (PCA) and deep belief networks (DBN) is proposed, namely PCA-DBN. Firstly, the second-order moment is used to analyze the time-domain of MI-EEG, select the effective time interval. Secondly, PCA is used to analyze the selected time-domain interval and obtain the principal component feature points. Then, feature points are imported into DBN to realize the final feature extraction. Finally, use the softmax classifier to complete task classification. Perform algorithm validation on the BCI Competition II Data set III and BCI Competition IV Data sets 2b, classification accuracies are 96.25% and 91.71%, kappa values are 0.925 and 0.8342. The paired-sample t-test with FDR correction is carried out on the verification results, and the comparison with some better classification algorithms shows that the algorithm has better performance. In the end, this method is used to extract the features of laboratory data, the optimal classification accuracy is 97.69% and kappa value is 0.9538, the validity of the method is further verified. INDEX TERMS Deep belief networks, motor imagery electroencephalogram, principal component analysis, second-order moment, softmax classifier.

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“…Generally, it is recommended to use at least five times more training data per class than the features [ 11 , 12 ]. Channel-frequency-time information makes the feature vector of EEG signal very high-dimensional [ 13 , 14 ]. These high-dimensional features necessitate the requirement for a large number of EEG epochs to be collected to train the classifier [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Multiclass Informative Instance Transfer Learning Framework for Motor Imagery-Based Brain-Computer Interface

Hossain

Khosravi

Hettiarachchi

et al. 2018

Computational Intelligence and Neuroscience

Self Cite

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A widely discussed paradigm for brain-computer interface (BCI) is the motor imagery task using noninvasive electroencephalography (EEG) modality. It often requires long training session for collecting a large amount of EEG data which makes user exhausted. One of the approaches to shorten this session is utilizing the instances from past users to train the learner for the novel user. In this work, direct transferring from past users is investigated and applied to multiclass motor imagery BCI. Then, active learning (AL) driven informative instance transfer learning has been attempted for multiclass BCI. Informative instance transfer shows better performance than direct instance transfer which reaches the benchmark using a reduced amount of training data (49% less) in cases of 6 out of 9 subjects. However, none of these methods has superior performance for all subjects in general. To get a generic transfer learning framework for BCI, an optimal ensemble of informative and direct transfer methods is designed and applied. The optimized ensemble outperforms both direct and informative transfer method for all subjects except one in BCI competition IV multiclass motor imagery dataset. It achieves the benchmark performance for 8 out of 9 subjects using average 75% less training data. Thus, the requirement of large training data for the new user is reduced to a significant amount.

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Motor Imagery Data Classification for BCI Application Using Wavelet Packet Feature Extraction

Cited by 20 publications

References 6 publications

Improving the Brain-Computer Interface Learning Process with Gamification in Motor Imagery: A Review

Improving the Brain-Computer Interface Learning Process with Gamification in Motor Imagery: A Review

A Motor Imagery EEG Feature Extraction Method Based on Energy Principal Component Analysis and Deep Belief Networks

Multiclass Informative Instance Transfer Learning Framework for Motor Imagery-Based Brain-Computer Interface

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