Decoding of Walking Imagery and Idle State Using Sparse Representation Based on fNIRS

Li, Hongquan; Gong, Anmin; Lei, Zhixin; Zhang, Wei; Wang, Fawang; Fu, Yunfa

doi:10.1155/2021/6614112

Cited by 5 publications

(6 citation statements)

References 39 publications

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“…For a certain category, when the residual is very small and the other categories are very large, the unknown category of the object belongs to that category [ 3 ]. The simplest sparse representation classification model is shown in Figure 6 .…”

Section: Methodsmentioning

confidence: 99%

“…According to the Global Burden of Disease Report, neurological and mental disorders account for four out of the six primary causes of years lived with disability, accounting for 33 percent of the years lived with disability and 13 percent of disability-adjusted life years (DALYs) [ 2 ]. People with walking disabilities need to improve their walking patterns or capability by using rehabilitation and assistive devices [ 3 ]. The brain-computer interface (BCI) is the best way to accommodate the neuro-rehabilitation process, by providing a communication pathway between the brain and the peripheral devices [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…For improving mild cognitive impairment (MCI), BCI based on functional near-infrared spectroscopy (fNIRS) had a positive result [ 3 ]. It has been widely used in the rehabilitation process [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI

Gulraiz

Naseer

Nazeer

et al. 2022

Sensors

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Brain-computer interface (BCI) systems based on functional near-infrared spectroscopy (fNIRS) have been used as a way of facilitating communication between the brain and peripheral devices. The BCI provides an option to improve the walking pattern of people with poor walking dysfunction, by applying a rehabilitation process. A state-of-the-art step-wise BCI system includes data acquisition, pre-processing, channel selection, feature extraction, and classification. In fNIRS-based BCI (fNIRS-BCI), channel selection plays a vital role in enhancing the classification accuracy of the BCI problem. In this study, the concentration of blood oxygenation (HbO) in a resting state and in a walking state was used to decode the walking activity and the resting state of the subject, using channel selection by Least Absolute Shrinkage and Selection Operator (LASSO) homotopy-based sparse representation classification. The fNIRS signals of nine subjects were collected from the left hemisphere of the primary motor cortex. The subjects performed the task of walking on a treadmill for 10 s, followed by a 20 s rest. Appropriate filters were applied to the collected signals to remove motion artifacts and physiological noises. LASSO homotopy-based sparse representation was used to select the most significant channels, and then classification was performed to identify walking and resting states. For comparison, the statistical spatial features of mean, peak, variance, and skewness, and their combination, were used for classification. The classification results after channel selection were then compared with the classification based on the extracted features. The classifiers used for both methods were linear discrimination analysis (LDA), support vector machine (SVM), and logistic regression (LR). The study found that LASSO homotopy-based sparse representation classification successfully discriminated between the walking and resting states, with a better average classification accuracy (p < 0.016) of 91.32%. This research provides a step forward in improving the classification accuracy of fNIRS-BCI systems. The proposed methodology may also be used for rehabilitation purposes, such as controlling wheelchairs and prostheses, as well as an active rehabilitation training technique for patients with motor dysfunction.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI

Gulraiz

Naseer

Nazeer

et al. 2022

Sensors

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show abstract

“…Due to low computational costs and high speed, LDA is often used as a classifier in fNIRS-BCI [ 47 , 48 , 49 , 50 , 51 ]. The basic idea of LDA is to project the data in low dimensions with the goal that the distance within a class is as close as possible and the distance between the classes is maximized to realize the classification.…”

Section: Methodsmentioning

confidence: 99%

Applying antagonistic activation pattern to the single-trial classification of mental arithmetic

Shixian

2022

Heliyon

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“…Tey could only obtain 67.9% average classifcation accuracy for three-class fNIRS signals. Sparse representation classifcation was also used to identify the appropriate features [53]. However, further research is still required to improve the subjectspecifc classifcation accuracy for multiclass activities for fNIRS-based BCI applications.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid GCN and Filter-Based Framework for Channel and Feature Selection: An fNIRS-BCI Study

Zafar

Kallu

Yaqub

et al. 2023

International Journal of Intelligent Systems

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In this study, a channel and feature selection methodology is devised for brain-computer interface (BCI) applications using functional near-infrared spectroscopy (fNIRS). A graph convolutional network (GCN) is employed to select the appropriate and correlated fNIRS channels. Furthermore, in the feature extraction phase, the performance of two filter-based feature selection algorithms, (i) the minimum redundancy maximum relevance (mRMR) and (ii) ReliefF, is investigated. The five most commonly used temporal statistical features (i.e., mean, slope, maximum, skewness, and kurtosis) are used, whereas the conventional support vector machine (SVM) is utilized as a classifier for training and testing. The proposed methodology is validated using an available online dataset of motor imagery (left- and right-hand), mental arithmetic, and baseline tasks. First, the efficacy of the proposed methodology is shown for two-class BCI applications (i.e., left- vs. right-hand motor imagery and mental arithmetic vs. baseline). Second, the proposed framework is applied to four-class BCI applications (i.e., left- vs. right-hand motor imagery vs. mental arithmetic vs. baseline). The results show that the number of appropriate channels and features was significantly reduced, resulting in a significant increase in classification accuracy for both two-class and four-class BCI applications, respectively. Furthermore, both mRMR (i.e., 87.8% for motor imagery, 87.1% for mental arithmetic, and 78.7% for four-class) and ReliefF (i.e., 90.7% for motor imagery, 93.7% for mental arithmetic, and 81.6% for four-class) yielded high average classification accuracy p < 0.05 . However, the results of the ReliefF algorithm are more stable and significant.

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Decoding of Walking Imagery and Idle State Using Sparse Representation Based on fNIRS

Cited by 5 publications

References 39 publications

LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI

LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI

Applying antagonistic activation pattern to the single-trial classification of mental arithmetic

A Hybrid GCN and Filter-Based Framework for Channel and Feature Selection: An fNIRS-BCI Study

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