A Haemodynamic Brain–Computer Interface Based on Real-Time Classification of near Infrared Spectroscopy Signals during Motor Imagery and Mental Arithmetic

Stangl, Matthias; Bauernfeind, Günther; Kurzmann, Jürgen; Scherer, Reinhold; Neuper, Christa

doi:10.1255/jnirs.1048

Cited by 38 publications

(44 citation statements)

References 68 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

220

159

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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

220

159

View full text Add to dashboard Cite

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“…Online classification is a critical step toward realworld BCI applications and presents various challenges not applicable to offline classification, including hardware and software adaptations to allow for immediate classification, and to address classifier generalization issues. 29 The online accuracies achieved in this study are on par with those reached by Schudlo et al, 29 and Coyle et al, 3 and exceed the accuracies of other online NIRS-BCI studies, such as those by Chan et al 82 and Stangl et al 83 Our training paradigm was similar to that of previous online NIRS-BCIs (i.e., used in Ref. 29) but with fewer samples for classifier training and a shorter task performance interval of 17 s compared to 20 s used by Schudlo et al 29 This shorter response interval can improve the communication rate and decrease the mental demand placed on BCI users.…”

Section: Online and Offline Classificationcontrasting

confidence: 55%

Usability and performance-informed selection of personalized mental tasks for an online near-infrared spectroscopy brain-computer interface

et al. 2015

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Abstract. Brain-computer interfaces (BCIs) allow individuals to use only cognitive activities to interact with their environment. The widespread use of BCIs is limited, due in part to their lack of user-friendliness. The main goal of this work was to develop a more user-centered BCI and determine if: (1) individuals can acquire control of an online near-infrared spectroscopy BCI via usability and performance-informed selection of mental tasks without compromising classification accuracy and (2) the combination of usability and performance-informed selection of mental tasks yields subjective ease-of-use ratings that exceed those attainable with prescribed mental tasks. Twenty able-bodied participants were recruited. Half of the participants served as a control group, using the state-of-the-art prescribed mental strategies. The other half of the participants comprised the study group, choosing their own personalized mental strategies out of eleven possible tasks. It was concluded that users were, in fact, able to acquire control of the more user-centered BCI without a significant change in accuracy compared to the prescribed task BCI. Furthermore, the personalized BCI yielded higher subjective ease-of-use ratings than the prescribed BCI. Average online accuracies of 77 AE 12.9% and 73 AE 12.9% were achieved by the personalized and prescribed mental task groups, respectively.

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“…This imaging modality has been used to detect the performance of a variety of controlled mental tasks including motor imagery tasks [2][3][4] and higherorder cognitive tasks [4][5][6][7][8][9][10][11][12][13][14] using measurements from the motor and prefrontal cortices, respectively. Individuals with congenital or long-term motor impairments may find it difficult or impossible to elicit a significant response in the motor cortex [15][16][17].…”

Section: Cortical Regions Considered In Nirs-bci Applicationsmentioning

confidence: 99%

“…Thus, cognitive tasks that are not motor-based may be more suitable for a larger variety of potential BCI users and will be the focus of this work. These types of tasks previously considered for NIRS-BCI control have included mental arithmetic [4,5,7,8,[10][11][12]14], mental singing [6][7][8]14], word generation [9,12], mental rotation [12,14], and the n-back task [13].…”

Section: Cortical Regions Considered In Nirs-bci Applicationsmentioning

confidence: 99%

“…In classifying higher-level cognitive tasks, a limited number of NIRS studies have considered measurements from brain regions other than or in addition to the anterior PFC [4,5,14]. To differentiate mental arithmetic and rest, Bauernfeind et al measured cortical activity from both the anterior and dorsolateral prefrontal cortices [5].…”

Section: Cortical Regions Considered In Nirs-bci Applicationsmentioning

confidence: 99%

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