Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients

Qureshi, Nauman Khalid; Naseer, Noman; Noori, Farzan Majeed; Nazeer, Hammad; Khan, Rayyan Azam; Saleem, Sajid

doi:10.3389/fnbot.2017.00033

Cited by 47 publications

(37 citation statements)

References 65 publications

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“…These filters have been shown to work well for several other tasks, for example, motor imagery, mental arithmetic etc. in previous studies [ 20 , 23 , 29 , 47 , 55 , 80 – 84 ].…”

Section: Resultsmentioning

confidence: 87%

“…Accordingly, there is always a delay between an activity performed and a detected response; thus, in such decoding tasks, classification accuracy is compromised. With advanced filtering techniques [ 11 , 93 , 94 ], different feature combinations [ 81 ] and various classification techniques [ 55 , 80 ], accuracies can be increased. One additional limitation of this study is that it generates the control command based on the walk intention whereas during the rest intention it holds the lower limb to its last updated position.…”

Section: Discussionmentioning

confidence: 99%

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fNIRS-based Neurorobotic Interface for gait rehabilitation

Khan

Naseer

Qureshi

et al. 2018

J NeuroEngineering Rehabil

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BackgroundIn this paper, a novel functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI) framework for control of prosthetic legs and rehabilitation of patients suffering from locomotive disorders is presented.MethodsfNIRS signals are used to initiate and stop the gait cycle, while a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control the torques of hip and knee joints for minimization of position error. In the present study, the brain signals of walking intention and rest tasks were acquired from the left hemisphere’s primary motor cortex for nine subjects. Thereafter, for removal of motion artifacts and physiological noises, the performances of six different filters (i.e. Kalman, Wiener, Gaussian, hemodynamic response filter (hrf), Band-pass, finite impulse response) were evaluated. Then, six different features were extracted from oxygenated hemoglobin signals, and their different combinations were used for classification. Also, the classification performances of five different classifiers (i.e. k-Nearest Neighbour, quadratic discriminant analysis, linear discriminant analysis (LDA), Naïve Bayes, support vector machine (SVM)) were tested.ResultsThe classification accuracies obtained from SVM using the hrf were significantly higher (p < 0.01) than those of the other classifier/ filter combinations. Those accuracies were 77.5, 72.5, 68.3, 74.2, 73.3, 80.8, 65, 76.7, and 86.7% for the nine subjects, respectively.ConclusionThe control commands generated using the classifiers initiated and stopped the gait cycle of the prosthetic leg, the knee and hip torques of which were controlled using the PD-CTC to minimize the position error. The proposed scheme can be effectively used for neurofeedback training and rehabilitation of lower-limb amputees and paralyzed patients.

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“…These filters have been shown to work well for several other tasks, for example, motor imagery, mental arithmetic etc. in previous studies [ 20 , 23 , 29 , 47 , 55 , 80 – 84 ].…”

Section: Resultsmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

fNIRS-based Neurorobotic Interface for gait rehabilitation

Khan

Naseer

Qureshi

et al. 2018

J NeuroEngineering Rehabil

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“…The latter was likely compounded by the observation from fMRI studies that MI-related activation in the SMA frequently occurs at a greater distance from the cortical surface (Monti et al, 2010;Taube et al, 2015). TR detection will help compensate for activation at greater depths ; however, these challenges reflect the lower classification accuracy generally reported for MI compared to tasks that activate the prefrontal cortex (Shin et al, 2017;Qureshi et al, 2017). It should also be noted that the activation contrast elicited by MI is less than for motor execution tasks (Batula et al, 2017), and activation for mental imagery tasks is not detectable in a small subset of participants, typically on the order of 10-15% (Fernández-Espejo et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…MI was the first task proposed for BCI applications, which requires participants to perform kinesthetic imagining, such as imagining squeezing a ball (Coyle et al, 2004), finger tapping (Sitaram et al, 2007), and hand grasping (Fazli et al, 2012). More recent fNIRS-BCI applications have focused on activation paradigms that involve the prefrontal cortex, such as mental arithmetic, to avoid signal loss due to the presence of hair and concerns regarding the quality of the NIRS signal for MI tasks (Shin et al, 2017;Qureshi et al, 2017). However, MI has proven extremely valuable in fMRI studies of DOC.…”

Section: Introductionmentioning

confidence: 99%

Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication

et al. 2020

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Brain-computer interfaces (BCIs) are becoming increasingly popular as a tool to improve the quality of life of patients with disabilities. Recently, time-resolved functional nearinfrared spectroscopy (TR-fNIRS) based BCIs are gaining traction because of their enhanced depth sensitivity leading to lower signal contamination from the extracerebral layers. This study presents the first account of TR-fNIRS based BCI for "mental communication" on healthy participants. Twenty-one (21) participants were recruited and were repeatedly asked a series of questions where they were instructed to imagine playing tennis for "yes" and to stay relaxed for "no." The change in the mean timeof-flight of photons was used to calculate the change in concentrations of oxy-and deoxyhemoglobin since it provides a good compromise between depth sensitivity and signal-to-noise ratio. Features were extracted from the average oxyhemoglobin signals to classify them as "yes" or "no" responses. Linear-discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the responses using the leave-one-out cross-validation method. The overall accuracies achieved for all participants were 75% and 76%, using LDA and SVM, respectively. The results also reveal that there is no significant difference in accuracy between questions. In addition, physiological parameters [heart rate (HR) and mean arterial pressure (MAP)] were recorded on seven of the 21 participants during motor imagery (MI) and rest to investigate changes in these parameters between conditions. No significant difference in these parameters was found between conditions. These findings suggest that TR-fNIRS could be suitable as a BCI for patients with brain injuries.

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“…While the use of short-separation signals has been shown to significantly improve the robustness of the estimation of hemodynamic response emerging from brain (Gagnon et al, 2011;Yücel et al, 2015), only 4% of the recent fNIRS-based BCI studies used short-separation measurements in their work (see Figure 3) and none applied it in a GLM framework which is the standard approach in neuroscience research today. Some other works, on the other hand, applied the GLM, albeit without shortseparation regression (such as Qureshi et al, 2017), and as a preprocessing step on the full dataset.…”

Section: Preprocessing In Fnirs-based Bci: An Overview and Perspectivementioning

confidence: 99%

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

Lühmann

Ortega-Martínez

Boas

et al. 2020

Front. Hum. Neurosci.

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Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to better classification accuracy. In this manuscript, we provide a brief introduction into the GLM and show how to incorporate it into a typical BCI preprocessing pipeline and cross-validation. Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. We show that the GLM-based approach can provide better single trial estimates of brain activity as well as a new feature type, i.e., the weight of the individual and channel-specific hemodynamic response function (HRF) regressor. The improved estimates yield features with higher separability, that significantly enhance accuracy in a binary classification task when compared to conventional preprocessing-on average +7.4% across subjects and feature types. We propose to adapt this well-established approach from neuroscience to the domain of single-trial analysis and preprocessing wherever the classification of evoked brain activity is of concern, for instance in BCI.

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Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients

Cited by 47 publications

References 65 publications

fNIRS-based Neurorobotic Interface for gait rehabilitation

fNIRS-based Neurorobotic Interface for gait rehabilitation

Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

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