Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface

Naseer, Noman; Hong, Melissa Jiyoun; Hong, Keum‐Shik

doi:10.1007/s00221-013-3764-1

Cited by 196 publications

(146 citation statements)

References 42 publications

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“…In contrast, features from fNIRS were classified with 84.15 ± 6.8, 82.4% ± 6.3 and 86% ± 7.2 accuracy, sensitivity and specificity respectively. These fNIRS results outperformed previous studies [47,[88][89][90][91][92][93][94][95][96][97][98]. It however needs to be cautious as the data sets were different.…”

Section: Discussionmentioning

confidence: 46%

Mental stress assessment using simultaneous measurement of EEG and fNIRS

Al-Shargie

Kiguchi

Badruddin

et al. 2016

Biomed. Opt. Express

196

148

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Previous studies reported mental stress as one of the major contributing factors leading to various diseases such as heart attack, depression and stroke. An accurate stress assessment method may thus be of importance to clinical intervention and disease prevention. We propose a joint independent component analysis (jICA) based approach to fuse simultaneous measurement of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) on the prefrontal cortex (PFC) as a means of stress assessment. For the purpose of this study, stress was induced by using an established mental arithmetic task under time pressure with negative feedback. The induction of mental stress was confirmed by salivary alpha amylase test. Experiment results showed that the proposed fusion of EEG and fNIRS measurements improves the classification accuracy of mental stress by +3.4% compared to EEG alone and +11% compared to fNIRS alone. Similar improvements were also observed in sensitivity and specificity of proposed approach over unimodal EEG/fNIRS. Our study suggests that combination of EEG (frontal alpha rhythm) and fNIRS (concentration change of oxygenated hemoglobin) could be a potential means to assess mental stress objectively. References and links1. J. Decker, "The Stress Syndrome," Am. J. Nurs. 65(3), 97-99 (1965). 2. L. R. Murphy, "Stress management in work settings: a critical review of the health effects," Am. J. Health Promot. 11(2), 112-135 (1996). 3. B. Czéh, T. Michaelis, T. Watanabe, J. Frahm, G. de Biurrun, M. van Kampen, A. Bartolomucci, and E. Fuchs, "Stress-induced changes in cerebral metabolites, hippocampal volume, and cell proliferation are prevented by antidepressant treatment with tianeptine," Proc. Natl. Acad. Sci. U.S.A. 98(22), 12796-12801 (2001). 4. C. M. Vander Weele, C. Saenz, J. Yao, S. S. Correia, and K. A. Goosens, "Restoration of hippocampal growth hormone reverses stress-induced hippocampal impairment," Front. Behav. Neurosci. 7, 66 (2013). 5. A. Vyas, R. Mitra, B. S. Shankaranarayana Rao, and S. Chattarji, "Chronic stress induces contrasting patterns of dendritic remodeling in hippocampal and amygdaloid neurons," J. Neurosci. 22(15), 6810-6818 (2002). 6. B. S. McEwen, "Central effects of stress hormones in health and disease: Understanding the protective and damaging effects of stress and stress mediators," Eur. J. Pharmacol. 583(2-3), 174-185 (2008). 7. P. C. Strike and A. Steptoe, "Systematic review of mental stress-induced myocardial ischaemia," Eur. Heart J.24(8), 690-703 (2003). 8. A. Tsutsumi, K. Kayaba, and S. Ishikawa, "Impact of occupational stress on stroke across occupational classes and genders," Soc. Sci. Med. 72(10), 1652-1658 (2011). 9. R. A. Ajjan and P. J. Grant, "Cardiovascular disease prevention in patients with type 2 diabetes: The role of oral anti-diabetic agents," Diab. Vasc. Dis. Res. 3(3), 147-158 (2006). 10. C. Hammen, "Stress and depression," Annu. Rev. Clin. Psychol. 1(1), 293-319 (2005 532-545 (1972). 22. T. G. Vrijkotte, L. J. van Doornen, and E. ...

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Section: Discussionmentioning

confidence: 46%

Mental stress assessment using simultaneous measurement of EEG and fNIRS

Al-Shargie

Kiguchi

Badruddin

et al. 2016

Biomed. Opt. Express

196

148

View full text Add to dashboard Cite

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“…Figure 2 shows the optodes configuration in this work. The NIRS signals were acquired using 7 sources and 16 detectors forming a combinational pair of 28 channels that were placed on the PFC and dorsolateral prefrontal cortex (DPFC) according to the International 10-20 System [14,30]. The data from the right DPFC were recorded through channels 1~8, and the data from the left DPFC were recorded by channels 21~28.…”

Section: Methodsmentioning

confidence: 99%

“…In an active BCI, the brain signal is made directly and intentionally by the user, which is independent of external events: Motion intention, motor imageries, and mental tasks fall into this category [11,[14][15][16][17][18][19][20][21]. In a reactive BCI, the brain signal is generated in reaction to external stimulation: All audio, video and pain stimuli generate a reactive signal in the brain that can be used for reactive BCI [3,[22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Passive BCI based on drowsiness detection: an fNIRS study

Khan

Hong

2015

Biomed. Opt. Express

Self Cite

180

135

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Abstract:We use functional near-infrared spectroscopy (fNIRS) to discriminate the alert and drowsy states for a passive brain-computer interface (BCI). The passive brain signals for the drowsy state are acquired from the prefrontal and dorsolateral prefrontal cortex. The experiment is performed on 13 healthy subjects using a driving simulator, and their brain activity is recorded using a continuous-wave fNIRS system. Linear discriminant analysis (LDA) is employed for training and testing, using the data from the prefrontal, left-and right-dorsolateral prefrontal regions. For classification, eight features are tested: mean oxyhemoglobin, mean deoxyhemoglobin, skewness, kurtosis, signal slope, number of peaks, sum of peaks, and signal peak, in 0~5, 0~10, and 0~15 second time windows, respectively. The results show that the best performance for classification is achieved using mean oxyhemoglobin, the signal peak, and the sum of peaks as features. The average accuracies in the right dorsolateral prefrontal cortex (83.1, 83.4 and 84.9% in the 0~5, 0~10 and 0~15 second time windows, respectively) show that the proposed method has an effective utility for detection of drowsiness for a passive BCI.

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“…Various modalities have been used to measure brain signals in BCI, including electroencephalography (EEG) [5,6], functional magnetic resonance imaging (fMRI) [7] and functional near infrared spectroscopy (fNIRS) [2,[8][9][10][11][12]. fNIRS is a novel non-invasive technique applied in BCI [13][14][15][16]. fNIRS is an optical imaging technique which uses light in the range of near infrared to assess brain activities [17].…”

Section: Introductionmentioning

confidence: 99%

Classification of Hemodynamic Responses Associated With Force and Speed Imagery for a Brain-Computer Interface

Yin

Jiang

et al. 2015

J Med Syst

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Functional near-infrared spectroscopy (fNIRS) is an emerging optical technique, which can assess brain activities associated with tasks. In this study, six participants were asked to perform three imageries of hand clenching associated with force and speed, respectively. Joint mutual information (JMI) criterion was used to extract the optimal features of hemodynamic responses. And extreme learning machine (ELM) was employed to be the classifier. ELM solved the major bottleneck of feedforward neural networks in learning speed, this classifier was easily implemented and less sensitive to specified parameters. The 2-class fNIRS-BCI system was firstly built with an average accuracy of 76.7 %, when all force and speed tasks were categorized as one class, respectively. The multi-class systems based on different levels of force and speed attempted to be investigated, the accuracies were moderate. This study provided a novel paradigm for establishing fNIRS-BCI system, and provided a possibility to produce more degrees of freedom in BCI system.

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Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface

Cited by 196 publications

References 42 publications

Mental stress assessment using simultaneous measurement of EEG and fNIRS

Mental stress assessment using simultaneous measurement of EEG and fNIRS

Passive BCI based on drowsiness detection: an fNIRS study

Classification of Hemodynamic Responses Associated With Force and Speed Imagery for a Brain-Computer Interface

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