Classification of prefrontal and motor cortex signals for three-class fNIRS–BCI

Hong, Keum–Shik; Naseer, Noman; Kim, Yun-Hee

doi:10.1016/j.neulet.2014.12.029

Cited by 241 publications

(168 citation statements)

References 39 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

197

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

197

148

View full text Add to dashboard Cite

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“…Currently, there are many methods to select optimal features for pattern recognition. The paper [22] investigated different time window combinations during the task period. The optimal time was selected to achieve a better accuracy.…”

Section: Discussionmentioning

confidence: 99%

Classification of Desired Motion Force Based On Cerebral Hemoglobin Information*

Sui

Zhang

et al. 2017

ITM Web Conf.

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Abstract:Strength training using patients' desired force level is helpful to improve training effect and promote rehabilitation. Generally, force levels are recognized by applying EMG or biomechanical information, these methods were not suitable for patients who lost important muscle groups or have weakened muscle functions. This paper proposed a method for identifying force level based on cerebral hemoglobin information, rather than the information depending on limbs. Ten subjects performed pedaling movement in three force levels. Features were extracted in both the time-domain and frequency-domain, with deoxygenated hemoglobin (deoxy) and the difference between oxygenated hemoglobin (oxy) and deoxy as parameters. Important frequency bands (0.01-0.03Hz, 0.03-0.06Hz, 0.06-0.09Hz, 0.09-0.12Hz) were confirmed by performing power spectrum density analysis. And significant measure channels were selected by performing one-way analyses of variance on three time periods around the start of movement. Force level was recognized by applying extreme learning machine (ELM). The corresponding precision rate was up to 78.7%. The proposed identification method was not restricted to the existence of limbs or the strength of limb information. It was realized based on brain information recorded in a real movement environment; it is helpful to realize the desired force level of subjects and to provide a control command for rehabilitation training equipment. IntroductionRecently, the number of people with motor dysfunction in lower limbs has been increasing rapidly because of natural disasters, injuries, accidents, diseases, and so on. In order to improve the training effect and the efficiency of recovery, patients' resistance training and active participation is very important and necessary. Thus, there is a great need to control rehabilitation device by patients' motion states of force level. Generally, EMG and other biomechanical information were used to recognize motion states [1][2][3]. However, many patients lost important muscles or have little muscle functions, and thus EMG or biomechanical information was not enough to identify motion states for these patients. Recently, brain information obtained by using EEG and fMRI technology has been very popular around the world [4-6]. Dario Farina, et al. classified four actual cycling torque motion by EEG information, and the recognition rate was around 50.8% [7]. DongEun Kim, et al. also applied EEG to classify the three grades (25%, 50%, 75%) of hand grip MVC (Maximum Voluntary Contraction), and the result was about 52.03% for left hand and 77.7% for right hand [8]. However, the above research had strict restrictions on testing environment and subjects. By contrast, functional near infrared spectroscopy (fNIRS) is a suitable tool to record brain information. Baolei Xu, et al. classified the speed and force clench imagination by support vector machine (SVM), the accuracy results was 72% [9]. Yunfa Fu, et al. combined the features of NIRS and EEG to recognize three levels of imagin...

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“…There are a number of methods to select the optimal features for constructing the feature set. In the literature [26], the researchers investigated different time window combinations during the task period. The optimal time window was selected to achieve a better accuracy.…”

Section: Feature Selectionmentioning

confidence: 99%

“…By defining different levels of force or speed, there provides a method for fNIRS-BCI system to generate more degrees of freedom. As we illustrated in section Introduction, Hong et al [26] established a three-class fNIRS-BCI system using the combination of prefrontal and motor cortex signals. Mental arithmetic vs. righthand motor imagery vs. left-hand motor imagery were classified with an accuracy of 75.6 %.…”

Section: Comparison With Other Workmentioning

confidence: 99%

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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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Classification of prefrontal and motor cortex signals for three-class fNIRS–BCI

Cited by 241 publications

References 39 publications

Mental stress assessment using simultaneous measurement of EEG and fNIRS

Mental stress assessment using simultaneous measurement of EEG and fNIRS

Classification of Desired Motion Force Based On Cerebral Hemoglobin Information*

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

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