Decoding Answers to Four-Choice Questions Using Functional near Infrared Spectroscopy

Naseer, Noman; Hong, Keum–Shik

doi:10.1255/jnirs.1145

Cited by 78 publications

(79 citation statements)

References 69 publications

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“…The most common features used for fNIRS are the mean of ∆HbO/∆HbR and the signal slope [7,58,59]. The mentioned features are used for the active type BCI tasks where the stimuli are given for a fixed time interval followed by a resting period.…”

Section: Discussionmentioning

confidence: 99%

Passive BCI based on drowsiness detection: an fNIRS study

Khan

Hong

2015

Biomed. Opt. Express

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183

136

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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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Section: Discussionmentioning

confidence: 99%

Passive BCI based on drowsiness detection: an fNIRS study

Khan

Hong

2015

Biomed. Opt. Express

Self Cite

183

136

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show abstract

“…where K is the number of wavelet packet coefficients for subspace s(j,n) and d n,i l,j (k) 2 and f 2 represent the energy density and energy of the wavelet packet, respectively. The energy of n-th wavelet packet subspace is calculated as:…”

Section: Selection Of Optimal Wavelet Packetsmentioning

confidence: 99%

“…People can control external devices by imagining the relevant action. Currently, BCI-based rehabilitation therapy is focused mainly on the recognition of motor imagery EEG (MI-EEG) [2][3][4] that is collected when a subject performs a specific motion imagination. However, the high dimension of the MI-EEG signal, which leads to high computational cost, usually brings an obstacle to the implementation of the on-line recognition algorithm and adversely affects the classification accuracy of MI-EEG.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap

Zhu

Liu

et al. 2017

Applied Sciences

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Abstract:Motor imagery EEG (MI-EEG), which reflects one's active movement intention, has attracted increasing attention in rehabilitation therapy, and accurate and fast feature extraction is the key problem to successful applications. Based on wavelet packet decomposition (WPD) and SE-isomap, an adaptive feature extraction method is proposed in this paper. The MI-EEG is preprocessed to determine a more effective time interval through average power spectrum analysis. WPD is then applied to the selected segment of MI-EEG, and the subject-based optimal wavelet packets (OWPs) with top mean variance difference are obtained autonomously. The OWP coefficients are further used to calculate the time-frequency features statistically and acquire the nonlinear manifold structure features, as well as the explicit nonlinear mapping, through SE-isomap. The hybrid features are obtained in a serial fusion way and evaluated by a k-nearest neighbor (KNN) classifier. The extensive experiments are conducted on a publicly available dataset, and the experiment results of 10-fold cross-validation show that the proposed method yields relatively higher classification accuracy and computation efficiency simultaneously compared with the commonly-used linear and nonlinear approaches.

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“…[14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] It can provide both topographic 15,16,[34][35][36] and tomographic 14,37 brain images. Speci¯cally, fNIRS monitors regional cerebral blood°ow (rCBF) variations by measuring, at wavelengths between 700 nm and 1000 nm, the near-infrared-lightabsorption changes caused by concentration variations of HbO and HbR, the two primary absorbing chromophores in brain capillary blood.…”

Section: Introductionmentioning

confidence: 99%

Detection of primary RGB colors projected on a screen using fNIRS

Liu

Hong

2017

J. Innov. Opt. Health Sci.

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In this study, functional near-infrared spectroscopy (fNIRS) is utilized to measure the hemodynamic responses (HRs) in the visual cortex of 14 subjects (aged 22-34 years) viewing the primary red, green, and blue (RGB) colors displayed on a white screen by a beam projector. The spatiotemporal characteristics of their oxygenated and deoxygenated hemoglobins (HbO and HbR) in the visual cortex are measured using a 15-source and 15-detector optode con¯guration. To see whether the activation maps upon RGB-color stimuli can be distinguished or not, the t-values of individual channels are averaged over 14 subjects. To¯nd the best combination of two features for classi¯cation, the HRs of activated channels are averaged over nine trials. The HbO mean, peak, slope, skewness and kurtosis values during 2-7 s window for a given 10 s stimulation period are analyzed. Finally, the linear discriminant analysis (LDA) for classifying three classes is applied. Individually, the best classi¯cation accuracy obtained with slope-skewness features was 74.07% (Subject 1), whereas the best overall over 14 subjects was 55.29% with peak-skewness combination. Noting that the chance level of 3-class classi¯cation is 33.33%, it can be said that RGB colors can be distinguished. The overall results reveal that fNIRS can be used for monitoring purposes of the HR patterns in the human visual cortex.

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Decoding Answers to Four-Choice Questions Using Functional near Infrared Spectroscopy

Cited by 78 publications

References 69 publications

Passive BCI based on drowsiness detection: an fNIRS study

Passive BCI based on drowsiness detection: an fNIRS study

Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap

Detection of primary RGB colors projected on a screen using fNIRS

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