Toward a fully integrated wireless wearable EEG-NIRS bimodal acquisition system

Safaie, Javad; Grebe, Reinhard; Moghaddam, Hamid Abrishami; Wallois, Fabrice

doi:10.1088/1741-2560/10/5/056001

Cited by 68 publications

(59 citation statements)

References 29 publications

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“…We anticipate and hope that converging efforts in Hybrid hardware integration (Safaie et al, 2013) and data analysis (Biessmann et al, 2011; Keles et al, 2016), potentially based on detailed knowledge of underlying physiology (Bari et al, 2012; Mandrick et al, 2016a), will lead to more effective passive BMIs and other applications in neuroergonomics.…”

Section: Discussionmentioning

confidence: 99%

Measuring Mental Workload with EEG+fNIRS

Aghajani

Garbey

Omurtag

2017

Front. Hum. Neurosci.

167

111

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We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL.

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Section: Discussionmentioning

confidence: 99%

Measuring Mental Workload with EEG+fNIRS

Aghajani

Garbey

Omurtag

2017

Front. Hum. Neurosci.

167

111

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“…Meanwhile NIRS signals are affected by dense hairs (Gervain et al, 2011) making EEG a better option for the detection of brain activities from the motor cortex region. Furthermore, if both modalities are positioned at the same brain location, they induce noise in each other thus reducing the strength of obtained signals for BCI (Safaie et al, 2013). Using the current setup, four signals were obtained thus enhancing the performance of NIRS by combining with EEG setup.…”

Section: Methodsmentioning

confidence: 99%

Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface

Khan

Hong

2014

Front. Hum. Neurosci.

232

153

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The hybrid brain-computer interface (BCI)'s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, “forward,” “backward,” “left,” and “right.” The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology.

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“…In 2013, a study of Safaie et al (2013) analyzed the steps involved in the development of hardware for a hybrid EEG–NIRS system, among which was the design of a wireless wearable module for simultaneous decoding of brain activity. In 2014, an optimal time window for hybrid EEG–NIRS features selection was investigated using a SSVEP-based paradigm (Tomita et al, 2014).…”

Section: Hardware Combinationmentioning

confidence: 99%

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

2017

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In this article, non-invasive hybrid brain–computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain–computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

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Toward a fully integrated wireless wearable EEG-NIRS bimodal acquisition system

Abstract: The device was tested and validated using our enhanced EEG-NIRS tissue mimicking fluid phantom for sensitivity mapping. Typical somatotopic electrical evoked potential experiments were performed to verify clinical applicability.

Cited by 68 publications

References 29 publications

Measuring Mental Workload with EEG+fNIRS

Measuring Mental Workload with EEG+fNIRS

Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

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