A Multichannel fNIRS System for Prefrontal Mental Task Classification with Dual-level Excitation and Deep Forest Algorithm

Chen, Cheng; Wen, Yizhen; Cui, Shaoyang; Qi, Xiangao; Liu, Zhenhong; Zhou, Linfeng; Chen, Mingyi; Zhao, Junbo; Wang, Guoxing

doi:10.1155/2020/1567567

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…Apart from these general technical challenges, researchers have to also deal with session and subject-wise differences in classifying the working memory-based varying workloads. These problems are classified as domain adaptation in the machine learning step where data from separate subjects as well as sessions are classified as belonging to different domains [30,[32][33][34][35]. The subsequent changes in the distribution of wavelets across separate domains, notwithstanding whether emanating from the subject or captured in different sessions, are classified as domain shift [36].…”

Section: Challenges In Fnirs-bcimentioning

confidence: 99%

Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)

et al. 2021

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Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system originally developed for continuous and non-invasive monitoring of brain function by measuring blood oxygen concentration. Recent advancements in brain–computer interfacing allow us to control the neuron function of the brain by combining it with fNIRS to regulate cognitive function. In this review manuscript, we provide information regarding current advancement in fNIRS and how it provides advantages in developing brain–computer interfacing to enable neuron function. We also briefly discuss about how we can use this technology for further applications.

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Section: Challenges In Fnirs-bcimentioning

confidence: 99%

Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)

et al. 2021

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“…Recent advances in BCI have led to a better understanding of neural functions and connections in the brain. BCI is an extensive study and requires knowledge of computer engineering, neuroscience, psychology, signal processing, and clinical rehabilitation [2].…”

Section: Introductionmentioning

confidence: 99%

Fusion of Deep Features from 2D-DOST of fNIRS Signals for Subject-Independent Classification of Motor Execution Tasks

Khani,

Solouk,

Kalbkhani

et al. 2023

International Journal of Intelligent Systems

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Functional near-infrared spectroscopy (fNIRS) is a low-cost and noninvasive method to measure the hemodynamic responses of cortical brain activities and has received great attention in brain-computer interface (BCI) applications. In this paper, we present a method based on deep learning and the time-frequency map (TFM) of fNIRS signals to classify the three motor execution tasks including right-hand tapping, left-hand tapping, and foot tapping. To simultaneously obtain the TFM and consider the correlation among channels, we propose to utilize the two-dimensional discrete orthonormal Stockwell transform (2D-DOST). The TFMs for oxygenated hemoglobin (HbO), reduced hemoglobin (HbR), and two linear combinations of them are obtained and then we propose three fusion schemes for combining their deep information extracted by the convolutional neural network (CNN). Two CNNs, LeNet and MobileNet, are considered and their structures are modified to maximize the accuracy. Due to the lack of enough signals for training CNNs, data augmentation based on the Wasserstein generative adversarial network (WGAN) is performed. Several simulations are performed to assess the performance of the proposed method in three-class and binary scenarios. The results present the efficiency of the proposed method in different scenarios. Also, the proposed method outperforms the recently introduced methods.

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“…In studies where the ROI is the PFC region, fNIRS signal classification is essential to the development of the fNIRS-BCI system. The classification of fNIRS signals acquired in the PFC has applications in many fields, including volitional control such as motor imagery (MI) (Ma et al, 2021 ), the identification of different emotions (Nguyen et al, 2021 ), the classification of mental workload levels (Lim et al, 2020 ), and the discrimination of intentional activity of the brain such as different mental tasks (Power et al, 2012 ; Chen et al, 2020 ). Most studies of fNIRS-BCI have focused on MI, affective responses, and mental workload, and less on mental task recognition.…”

Section: Introductionmentioning

confidence: 99%

Combining robust level extraction and unsupervised adaptive classification for high-accuracy fNIRS-BCI: An evidence on single-trial differentiation between mentally arithmetic- and singing-tasks

et al. 2022

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Functional near-infrared spectroscopy (fNIRS) is a safe and non-invasive optical imaging technique that is being increasingly used in brain-computer interfaces (BCIs) to recognize mental tasks. Unlike electroencephalography (EEG) which directly measures neural activation, fNIRS signals reflect neurovascular-coupling inducing hemodynamic response that can be slow in time and varying in the pattern. The established classifiers extend the EEG-ones by mostly employing the feature based supervised models such as the support vector machine (SVM) and linear discriminant analysis (LDA), and fail to timely characterize the level-sensitive hemodynamic pattern. A dedicated classifier is desired for intentional activity recognition of fNIRS-BCI, including the adaptive acquisition of response relevant features and accurate discrimination of implied ideas. To this end, we herein propose a specifically-designed joint adaptive classification method that combines a Kalman filtering (KF) for robust level extraction and an adaptive Gaussian mixture model (a-GMM) for enhanced pattern recognition. The simulative investigations and paradigm experiments have shown that the proposed KF/a-GMM classification method can effectively track the random variations of task-evoked brain activation patterns, and improve the accuracy of single-trial classification task of mental arithmetic vs. mental singing, as compared to the conventional methods, e.g., those that employ combinations of the band-pass filtering (BPF) based feature extractors (mean, slope, and variance, etc.) and the classical recognizers (GMM, SVM, and LDA). The proposed approach paves a promising way for developing the real-time fNIRS-BCI technique.

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A Multichannel fNIRS System for Prefrontal Mental Task Classification with Dual-level Excitation and Deep Forest Algorithm

Cited by 8 publications

References 26 publications

Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)

Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)

Fusion of Deep Features from 2D-DOST of fNIRS Signals for Subject-Independent Classification of Motor Execution Tasks

Combining robust level extraction and unsupervised adaptive classification for high-accuracy fNIRS-BCI: An evidence on single-trial differentiation between mentally arithmetic- and singing-tasks

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