Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method

Nazeer, Hammad; Naseer, Noman; Mehboob, Aakif; Khan, Muhammad Jawad; Khan, Rayyan Azam; Khan, Umar Shahbaz; Ayaz, Yasar

doi:10.3390/s20236995

Cited by 15 publications

(12 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the channel selection method used for EEG-BCI, the classification accuracy was 93.08%, by selecting only eight channels out of 64 when classifying motor imagery tasks [ 48 ]. A similar study was performed to select cortical activation-based channel selection using the z -score method for fNIRS-BCI problems, achieving a classification accuracy of 88% [ 21 ]. LASSO homotopy-based SRC autonomously selects the most significant channels for the fNIRS-BCI system, thus greatly improving the overall classification accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Selecting channels of interest (COI) or a region of interest (ROI) in BCI can save processing time, reduce dimensionality, improve performance, and provide adequate brain region identification with low noise signals. In the literature, the z -score approach, which uses cross-correlation and z -scores for ROI/COI selection, was utilized to improve the performance of the fNIRS-BCI system [ 21 ]. The hemodynamic responses with positive t -values were selected by using the t -value method [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

“…For the classification of different brain activity, fNIRS-based BCI mostly extracted features such as mean, peak, variance, skewness, kurtosis values, etc., from the obtained data [ 20 ]. In the literature, studies have been performed using single, multiple or a combination of features to classify two- or multiple-class fNIRS-BCI problems [ 21 ]. Support vector machine (SVM) and linear discrimination analysis (LDA) are mainly used to classify walking and resting states, but the classification accuracy is low and needs to be improved [ 22 ].…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI

Gulraiz

Naseer

Nazeer

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this study, a feature combination of signal mean, signal peak, and signal variance a was used for the ML classifiers. This specific combination was selected based on the higher classification accuracies that were obtained using these features [46,47].…”

Section: Feature Extractionmentioning

confidence: 99%

Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks

Hamid

Naseer

Nazeer

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

This research presents a brain–computer interface (BCI) framework for brain signal classificationusing deep learning (DL) and machine learning (ML) approaches on functional near-infrared spectroscopy (fNIRS) signals. fNIRS signals of motor execution for walking and rest tasks are acquired from the primary motor cortex in the brain’s left hemisphere for nine subjects. DL algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM) are used to achieve average classification accuracies of 88.50%, 84.24%, and 85.13%, respectively. For comparison purposes, three conventional ML algorithms, support vector machine (SVM), k-nearest neighbor (k-NN), and linear discriminant analysis (LDA) are also used for classification, resulting in average classification accuracies of 73.91%, 74.24%, and 65.85%, respectively. This study successfully demonstrates that the enhanced performance of fNIRS-BCI can be achieved in terms of classification accuracy using DL approaches compared to conventional ML approaches. Furthermore, the control commands generated by these classifiers can be used to initiate and stop the gait cycle of the lower limb exoskeleton for gait rehabilitation.

show abstract

“…In the case of classifcation accuracy, the selection of appropriate channels and features plays a vital role [22][23][24]. For active channel selection, averaging over all channels [25][26][27], averaging over a region of interest [28,29], t-and z-statistics [29][30][31][32], baseline correction [33], vector-phase analysis [31,[34][35][36], Pearson correlation coefcient [37], the contrast-to-noise ratio [38], LASSO homotopy-based sparse representation [39], and jointchannel-connectivity [40] methods are employed in fNIRSbased BCI studies. Temporal statistical characteristics of fNIRS signals time series (i.e., mean, slope, peak, minimum value, skewness, kurtosis, variance, and standard deviation) are the most commonly used features [6].…”

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

View full text Add to dashboard Cite

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.

show abstract

Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method

Cited by 15 publications

References 61 publications

LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI

LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI

Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks

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

Contact Info

Product

Resources

About