Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces

Assessing Time-Resolved fNIRS for Brain-Computer Interface Applications of Mental Communication

Brain-computer interfaces (BCIs) are becoming increasingly popular as a tool to improve the quality of life of patients with disabilities. Recently, time-resolved functional near-infrared spectroscopy (TR-fNIRS) based BCIs are gaining traction because of their enhanced depth sensitivity leading to lower signal contamination from the extracerebral layers. This study presents the first account of TR-fNIRS based BCI for “mental communication” on healthy participants. Twenty-one (21) participants were recruited and were repeatedly asked a series of questions where they were instructed to imagine playing tennis for “yes” and to stay relaxed for “no.” The change in the mean time-of-flight of photons was used to calculate the change in concentrations of oxy- and deoxyhemoglobin since it provides a good compromise between depth sensitivity and signal-to-noise ratio. Features were extracted from the average oxyhemoglobin signals to classify them as “yes” or “no” responses. Linear-discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the responses using the leave-one-out cross-validation method. The overall accuracies achieved for all participants were 75% and 76%, using LDA and SVM, respectively. The results also reveal that there is no significant difference in accuracy between questions. In addition, physiological parameters [heart rate (HR) and mean arterial pressure (MAP)] were recorded on seven of the 21 participants during motor imagery (MI) and rest to investigate changes in these parameters between conditions. No significant difference in these parameters was found between conditions. These findings suggest that TR-fNIRS could be suitable as a BCI for patients with brain injuries.

An EEG-fNIRS hybridization technique in the four-class classification of alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is projected to become one of the most expensive diseases in modern history, and yet diagnostic uncertainties exist that can only be confirmed by postmortem brain examination. Machine Learning (ML) algorithms have been proposed as a feasible alternative to the diagnosis of several neurological diseases and disorders, such as AD. An ideal ML-derived diagnosis should be inexpensive and noninvasive while retaining the accuracy and versatility that make ML techniques desirable for medical applications.

NEW METHODS

Two portable modalities, Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) have been widely employed in constructing hybrid classification models to compensate for each other's weaknesses. In this study, we present a hybrid EEG-fNIRS model for classifying four classes of subjects including one healthy control (HC) group, one mild cognitive impairment (MCI) group, and, two AD patient groups. A concurrent EEG-fNIRS setup was used to record data from 29 subjects during a random digit encoding-retrieval task. EEG-derived and fNIRS-derived features were sorted using a Pearson correlation coefficient-based feature selection (PCCFS) strategy and then fed into a linear discriminant analysis (LDA) classifier to evaluate their performance.

RESULTS

The hybrid EEG-fNIRS feature set was able to achieve a higher accuracy (79.31 %) by integrating their complementary properties, compared to using EEG (65.52 %) or fNIRS alone (58.62 %). Moreover, our results indicate that the right prefrontal and left parietal regions are associated with the progression of AD.

COMPARISON WITH EXISTING METHODS

Our hybrid and portable system provided enhanced classification performance in multi-class classification of AD population.

CONCLUSIONS

These findings suggest that hybrid EEG-fNIRS systems are a promising tool that may enhance the AD diagnosis and assessment process.

Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes

This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky-Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.

EEG- and EOG-Based Asynchronous Hybrid BCI: A System Integrating a Speller, a Web Browser, an E-Mail Client, and a File Explorer

This paper presents a new asynchronous hybrid brain-computer interface (BCI) system that integrates a speller, a web browser, an e-mail client, and a file explorer using electroencephalographic (EEG) and electrooculography (EOG) signals. More specifically, an EOG-based button selection method, which requires the user to blink his/her eyes synchronously with the target button's flashes during button selection, is first presented. Next, we propose a mouse control method by combining EEG and EOG signals, in which the left-/right-hand motor imagery (MI)-related EEG is used to control the horizontal movement of the mouse and the blink-related EOG is used to control the vertical movement of the mouse and to select/reject a target. These two methods are further combined to develop the integrated hybrid BCI system. With the hybrid BCI, users can input text, access the internet, communicate with others via e-mail, and manage files in their computer using only EEG and EOG without any body movements. Ten healthy subjects participated in a comprehensive online experiment, and superior performance was achieved compared with our previously developed P300- and MI-based BCI and some other asynchronous BCIs, therefore demonstrating the system's effectiveness.

Simulation and in vivo investigation of light-emitting diode, near infrared Gaussian beam profiles

Near infrared spectroscopy is an optical imaging technique which offers a non-invasive, portable, and low-cost method for continuously measuring the oxygenation of tissues. In particular, it can provide the brain activation through measuring the blood oxygenation and blood volume in the cortex. Understanding and then improving the spatial and depth sensitivity of near infrared spectroscopy measurements to brain tissue are essential for designing experiments as well as interpreting research findings. In this study, we investigate the effect of applying two common light beam profiles including Uniform and Gaussian on the penetration depth of an LED-based near infrared spectroscopy. In this regard, two Gaussian profiles were produced by adjusting plano-convex and bi-convex lenses and the Uniform profile was provided by applying a flat lens. Two experiments were conducted in this study. First, a simulation experiment was carried out based on scanning the intra space of a liquid phantom by using static and pulsating absorbers to compare the penetration depth of the configurations applied on the LED-based near infrared spectroscopy with that of a laser-based near infrared spectroscopy. Second, to show the feasibility of the best proposed configuration applied, an in vivo experiment of stress assessment has been performed and its results have been compared with that results obtained by laser one. The results showed that the LED-based near infrared spectroscopy equipped with bi-convex lens provides a penetration depth and hence quality measurements of near infrared spectroscopy and its extracted heart rate variability signals as well as laser-based near infrared spectroscopy especially in the application of stress assessment.

A Piezoresistive Array Armband With Reduced Number of Sensors for Hand Gesture Recognition

Human machine interfaces (HMIs) are employed in a broad range of applications, spanning from assistive devices for disability to remote manipulation and gaming controllers. In this study, a new piezoresistive sensors array armband is proposed for hand gesture recognition. The armband encloses only three sensors targeting specific forearm muscles, with the aim to discriminate eight hand movements. Each sensor is made by a force-sensitive resistor (FSR) with a dedicated mechanical coupler and is designed to sense muscle swelling during contraction. The armband is designed to be easily wearable and adjustable for any user and was tested on 10 volunteers. Hand gestures are classified by means of different machine learning algorithms, and classification performances are assessed applying both, the 10-fold and leave-one-out cross-validations. A linear support vector machine provided 96% mean accuracy across all participants. Ultimately, this classifier was implemented on an Arduino platform and allowed successful control for videogames in real-time. The low power consumption together with the high level of accuracy suggests the potential of this device for exergames commonly employed for neuromotor rehabilitation. The reduced number of sensors makes this HMI also suitable for hand-prosthesis control.

A Between-Subject fNIRS-BCI Study on Detecting Self-Regulated Intention During Walking

OBJECTIVE

Most BCI (brain-computer interface) studies have focused on detecting motion intention from a resting state. However, the dynamic regulation of two motion states, which usually happens in real life, is rarely studied. Besides, popular within-subject methods also require an extensive and time-consuming learning stage when testing on a new subject. This paper proposed a method to discriminate dynamic gait- adjustment intention with strong adaptability for different subjects.

METHODS

Cerebral hemoglobin signals obtained from 30 subjects were studied to decode gait-adjustment intention. Cerebral hemoglobin information was recorded by using fNIRS (functional near infrared spectroscopy) technology. Mathematical morphology filtering was applied to remove zero drift and EWM (Entropy Weight Method) was used to calculate the average hemoglobin values over Regions of Interest (ROIs). The gradient boosting decision tree (GBDT) was utilized to detect the onset of self-regulated intention. A 2-layer-GA-SVM (Genetic Algorithm-Support Vector Machine) model based on stacking algorithm was further proposed to identify the four types of self-regulated intention (speed increase, speed reduction, step increase, and step reduction).

RESULTS

It was found that GBDT had a good performance to detect the onset intention with an average AUC (Area Under Curve) of 0.894. The 2-layer-GA-SVM model boosted the average ACC (accuracy) of four types of intention from 70.6% to 84.4% ( p = 0.005 ) from the single GA-SVM model. Furthermore, the proposed method passed pseudo-online test with the average results as following: AUC = 0.883, TPR (True Positive Rate) = 97.5%, FPR (False Positive Rate) = 0.11%, and LAY (Detection Latency) = -0.52 ± 2.57 seconds for the recognition of gait-adjustment intention; ACC = 80% for the recognition of adjusted gait.

CONCLUSION

The results indicate that it is feasible to decode dynamic gait-adjustment intentions from a motion state for different subjects based on fNIRS technology. It has a potential to realize the practical application of fNIRS-based brain-computer interface technology in controlling walking-assistive devices.

Indoor Simulated Training Environment for Brain-Controlled Wheelchair Based on Steady-State Visual Evoked Potentials

Brain-controlled wheelchair (BCW) has the potential to improve the quality of life for people with motor disabilities. A lot of training is necessary for users to learn and improve BCW control ability and the performances of BCW control are crucial for patients in daily use. In consideration of safety and efficiency, an indoor simulated training environment is built up in this paper to improve the performance of BCW control. The indoor simulated environment mainly realizes BCW implementation, simulated training scenario setup, path planning and recommendation, simulated operation, and scoring. And the BCW is based on steady-state visual evoked potentials (SSVEP) and the filter bank canonical correlation analysis (FBCCA) is used to analyze the electroencephalography (EEG). Five tasks include individual accuracy, simple linear path, obstacles avoidance, comprehensive steering scenarios, and evaluation task are designed, 10 healthy subjects were recruited and carried out the 7-days training experiment to assess the performance of the training environment. Scoring and command-consuming are conducted to evaluate the improvement before and after training. The results indicate that the average accuracy is 93.55% and improves from 91.05% in the first stage to 96.05% in the second stage (p = 0.001). Meanwhile, the average score increases from 79.88 in the first session to 96.66 in the last session and tend to be stable (p < 0.001). The average number of commands and collisions to complete the tasks decreases significantly with or without the approximate shortest path (p < 0.001). These results show that the performance of subjects in BCW control achieves improvement and verify the feasibility and effectiveness of the proposed simulated training environment.

Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction

Brain-computer interfaces (BCIs) have long been seen as control interfaces that translate changes in brain activity, produced either by means of a volitional modulation or in response to an external stimulation. However, recent trends in the BCI and neurofeedback research highlight passive monitoring of a user's brain activity in order to estimate cognitive load, attention level, perceived errors and emotions. Extraction of such higher order information from brain signals is seen as a gateway for facilitation of interaction between humans and intelligent systems. Particularly in the field of robotics, passive BCIs provide a promising channel for prediction of user's cognitive and affective state for development of a user-adaptive interaction. In this paper, we first illustrate the state of the art in passive BCI technology and then provide examples of BCI employment in human-robot interaction (HRI). We finally discuss the prospects and challenges in integration of passive BCIs in socially demanding HRI settings. This work intends to inform HRI community of the opportunities offered by passive BCI systems for enhancement of human-robot interaction while recognizing potential pitfalls.

Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features

Online accurate estimation of remaining useful life (RUL) of lithium-ion batteries is a necessary feature of any smart battery management system (BMS). In this paper, a novel partial discharge data (PDD)-based support vector machine (SVM) model is proposed for RUL prediction. The proposed algorithm extracts the critical features from the voltage and temperature of PDD to train the SVM models. The classification and regression attributes of SVM are utilized to classify and predict accurate RUL. The different ranges of PDD were analyzed to find the optimal range for training the SVM model. The SVM model trained with optimal PDD features classifies the RUL into six different classes for gross estimation, and the support vector regression is used to estimate the accurate value of the last class. The classification and predictive performance of SVM model trained using the full discharge data and PDD are compared for publicly available data. Results show that the SVM classification and regression model trained with PDD features can accurately predict the RUL with low storage pressure on BMS. The PDD-based SVM model can be utilized for online RUL estimation in electric vehicles.

Prediction in Autism by Deep Learning Short-Time Spontaneous Hemodynamic Fluctuations

This study aims to explore the possibility of using a multilayer artificial neural network for the classification between children with autism spectrum disorder (ASD) and typically developing (TD) children based on short-time spontaneous hemodynamic fluctuations. Spontaneous hemodynamic fluctuations were collected by a functional near-infrared spectroscopy setup from bilateral inferior frontal gyrus and temporal cortex in 25 children with ASD and 22 TD children. To perform feature extraction and classification, a multilayer neural network called CGRNN was used which combined a convolution neural network (CNN) and a gate recurrent unit (GRU), since CGRNN has a strong ability in finding characteristic features and acquiring intrinsic relationship in time series. For the training and predicting, short-time (7 s) time-series raw functional near-infrared spectroscopy (fNIRS) signals were used as the input of the network. To avoid the over-fitting problem and effectively extract useful differentiation features from a sample with a very limited size (e.g., 25 ASDs and 22 TDs), a sliding window approach was utilized in which the initially recorded long-time (e.g., 480 s) time-series was divided into many partially overlapped short-time (7 s) sequences. By using this combined deep-learning network, a high accurate classification between ASD and TD could be achieved even with a single optical channel, e.g., 92.2% accuracy, 85.0% sensitivity, and 99.4% specificity. This result implies that the multilayer neural network CGRNN can identify characteristic features associated with ASD even in a short-time spontaneous hemodynamic fluctuation from a single optical channel, and second, the CGRNN can provide highly accurate prediction in ASD.

Toward a Hybrid Passive BCI for the Modulation of Sustained Attention Using EEG and fNIRS

We report results of a study that utilizes a BCI to drive an interactive interface countermeasure that allows users to self-regulate sustained attention while performing an ecologically valid, long-duration business logistics task. An engagement index derived from EEG signals was used to drive the BCI while fNIRS measured hemodynamic activity for the duration of the task. Participants (n = 30) were split into three groups (1) no countermeasures (NOCM), (2) continuous countermeasures (CCM), and (3) event synchronized, level-dependent countermeasures (ECM). We hypothesized that the ability to self-regulate sustained attention through a neurofeedback mechanism would result in greater task engagement, decreased error rate and improved task performance. Data were analyzed by wavelet coherence analysis, statistical analysis, performance metrics and self-assessed cognitive workload via RAW-TLX. We found that when the BCI was used to deliver continuous interface countermeasures (CCM), task performance was moderately enhanced in terms of total 14,785 (σ = 423) and estimated missed sales 7.46% (σ = 1.76) when compared to the NOCM 14,529 (σ = 510), 9.79% (σ = 2.75), and the ECM 14,180 (σ = 875), 9.62% (σ = 4.91) groups. An "actions per minute" (APM) metric was used to determine interface interaction activity which showed that overall the CCM and ECM groups had a higher APM of 3.460 (SE = 0.140) and 3.317 (SE = 0.139) respectively when compared with the NOCM group 2.65 (SE = 0.097). Statistical analysis showed a significant difference between ECM - NOCM and CCM - NOCM (p < 0.001) groups, but no significant difference between the ECM - CCM groups. Analysis of the RAW-TLX scores showed that the CCM group had lowest total score 7.27 (σ = 3.1) when compared with the ECM 9.7 (σ = 3.3) and NOCM 9.2 (σ = 3.4) groups. No statistical difference was found between the RAW-TLX or the subscales, except for self-perceived performance (p < 0.028) comparing the CCM and ECM groups. The results suggest that providing a means to self-regulate sustained attention has the potential to keep operators engaged over long periods, and moderately increase on-task performance while decreasing on-task error.

A Newcomer's Guide to Functional Near Infrared Spectroscopy Experiments

This review presents a practical primer for functional near-infrared spectroscopy (fNIRS) with respect to technology, experimentation, and analysis software. Its purpose is to jump-start interested practitioners considering utilizing a non-invasive, versatile, nevertheless challenging window into the brain using optical methods. We briefly recapitulate relevant anatomical and optical foundations and give a short historical overview. We describe competing types of illumination (trans-illumination, reflectance, and differential reflectance) and data collection methods (continuous wave, time domain and frequency domain). Basic components (light sources, detection, and recording components) of fNIRS systems are presented. Advantages and limitations of fNIRS techniques are offered, followed by a list of very practical recommendations for its use. A variety of experimental and clinical studies with fNIRS are sampled, shedding light on many brain-related ailments. Finally, we describe and discuss a number of freely available analysis and presentation packages suited for data analysis. In conclusion, we recommend fNIRS due to its ever-growing body of clinical applications, state-of-the-art neuroimaging technique and manageable hardware requirements. It can be safely concluded that fNIRS adds a new arrow to the quiver of neuro-medical examinations due to both its great versatility and limited costs.

Modeling and classification of voluntary and imagery movements for brain-computer interface from fNIR and EEG signals through convolutional neural network

Practical brain-computer interface (BCI) demands the learning-based adaptive model that can handle diverse problems. To implement a BCI, usually functional near-infrared spectroscopy (fNIR) is used for measuring functional changes in brain oxygenation and electroencephalography (EEG) for evaluating the neuronal electric potential regarding the psychophysiological activity. Since the fNIR modality has an issue of temporal resolution, fNIR alone is not enough to achieve satisfactory classification accuracy as multiple neural stimuli are produced by voluntary and imagery movements. This leads us to make a combination of fNIR and EEG with a view to developing a BCI model for the classification of the brain signals of the voluntary and imagery movements. This work proposes a novel approach to prepare functional neuroimages from the fNIR and EEG using eight different movement-related stimuli. The neuroimages are used to train a convolutional neural network (CNN) to formulate a predictive model for classifying the combined fNIR-EEG data. The results reveal that the combined fNIR-EEG modality approach along with a CNN provides improved classification accuracy compared to a single modality and conventional classifiers. So, the outcomes of the proposed research work will be very helpful in the implementation of the finer BCI system.

Evaluation of Neural Degeneration Biomarkers in the Prefrontal Cortex for Early Identification of Patients With Mild Cognitive Impairment: An fNIRS Study

Mild cognitive impairment (MCI), a condition characterizing poor cognition, is associated with aging and depicts early symptoms of severe cognitive impairment, known as Alzheimer's disease (AD). Meanwhile, early detection of MCI can prevent progression to AD. A great deal of research has been performed in the past decade on MCI detection. However, availability of biomarkers for MCI detection requires greater attention. In our study, we evaluated putative and reliable biomarkers for diagnosing MCI by performing different mental tasks (i.e., N-back task, Stroop task, and verbal fluency task) using functional near-infrared spectroscopy (fNIRS) signals on a group of 15 MCI patients and 9 healthy control (HC). The 15 digital biomarkers (i.e., five means, seven slopes, peak, skewness, and kurtosis) and two image biomarkers (t-map, correlation map) in the prefrontal cortex (PFC) (i.e., left PFC, middle PFC, and right PFC) between the MCI and HC groups were investigated by the statistical analysis, linear discriminant analysis (LDA), and convolutional neural network (CNN) individually. The results reveal that the statistical analysis using digital biomarkers (with a p-value < 0.05) could not distinguish the MCI patients from the HC over 60% accuracy. Therefore, the current statistical analysis needs to be improved to be used for diagnosing the MCI patients. The best accuracy with LDA was 76.67% with the N-back and Stroop tasks. However, the CNN classification results trained by image biomarkers showed a high accuracy. In particular, the CNN results trained via t-maps revealed the best accuracy (90.62%) with the N-back task, whereas the CNN result trained by the correlation maps was 85.58% with the N-back task. Also, the results illustrated that investigating the sub-regions (i.e., right, middle, left) of the PFC for detecting MCI would be better than examining the whole PFC. The t-map (or/and the correlation map) is conclusively recommended as an image biomarker for early detection of AD. The combination of CNN and image biomarkers can provide a reliable clinical tool for diagnosing MCI patients.

A Mini-Review on Functional Near-Infrared Spectroscopy (fNIRS): Where Do We Stand, and Where Should We Go?

This mini-review is aimed at briefly summarizing the present status of functional near-infrared spectroscopy (fNIRS) and predicting where the technique should go in the next decade. This mini-review quotes 33 articles on the different fNIRS basics and technical developments and 44 reviews on the fNIRS applications published in the last eight years. The huge number of review articles about a wide spectrum of topics in the field of cognitive and social sciences, functional neuroimaging research, and medicine testifies to the maturity achieved by this non-invasive optical vascular-based functional neuroimaging technique. Today, fNIRS has started to be utilized on healthy subjects while moving freely in different naturalistic settings. Further instrumental developments are expected to be done in the near future to fully satisfy this latter important aspect. In addition, fNIRS procedures, including correction methods for the strong extracranial interferences, need to be standardized before using fNIRS as a clinical tool in individual patients. New research avenues such as interactive neurosciences, cortical activation modulated by different type of sport performance, and cortical activation during neurofeedback training are highlighted.

Using Polygraph to Detect Passengers Carrying Illegal Items

The present study examined the effectiveness of a Modified-Comparison Questions Technique, used in conjunction with the polygraph, to differentiate between common travelers, drug traffickers, and terrorists at transportation hubs. Two experiments were conducted using a mock crime paradigm. In Experiment 1, we randomly assigned 78 participants to either a drug condition, where they packed and lied about illicit drugs in their luggage, or a control condition, where they did not pack or lie about any illegal items. In Experiment 2, we randomly assigned 164 participants to one of the two conditions in Experiment 1 or an additional bomb condition, where they packed and lied about a bomb in their luggage. For both experiments, we assessed participants’ RR interval, heart rate, peak-to-peak amplitude of Galvanic Skin Response (GSR) and all three combined, using Discriminant Analyses to determine the classification accuracy of participants in each condition. In both experiments, we found decelerated heart rates and increased peak-to-peak amplitude of GSR in guilty participants when lying in response to questions regarding their crime. We also found accurate classifications of participants, in both Experiment 1 (drug vs. control: 84.2% vs. 82.5%) and Experiment 2 (drug vs. control: 82:1% vs. 95.1%; bomb vs. control: 93.2% vs. 95.1%; drug vs. bomb: 92.3% vs. 90.9%), above chance level. These findings indicate that Modified-CQT, combined with a polygraph test, is a viable method for investigating suspects of drug trafficking and terrorism at transportation hubs such as train stations and airports.

EEG-fTCD hybrid brain-computer interface using template matching and wavelet decomposition

OBJECTIVE

We aim at developing a hybrid brain-computer interface that utilizes electroencephalography (EEG) and functional transcranial Doppler (fTCD). In this hybrid BCI, EEG and fTCD are used simultaneously to measure electrical brain activity and cerebral blood velocity respectively in response to flickering mental rotation (MR) and word generation (WG) tasks. In this paper, we improve both the accuracy and information transfer rate (ITR) of this novel hybrid brain computer interface (BCI) we designed in our previous work.

APPROACH

To achieve such aim, we extended our feature extraction approach through using template matching and multi-scale analysis to extract EEG and fTCD features, respectively. In particular, template matching was used to analyze EEG data whereas 5-level wavelet decomposition was applied to fTCD data. Significant EEG and fTCD features were selected using Wilcoxon signed rank test. Support vector machines classifier (SVM) was used to project EEG and fTCD selected features of each trial into scalar SVM scores. Moreover, instead of concatenating EEG and fTCD feature vectors corresponding to each trial, we proposed a Bayesian fusion approach of EEG and fTCD evidences.

MAIN RESULTS

Average accuracy and average ITR of 98.11% and 21.29 bits min^-1 were achieved for WG versus MR classification while MR versus baseline yielded 86.27% average accuracy and 8.95 bit min^-1 average ITR. In addition, average accuracy of 85.29% and average ITR of 8.34 bits min^-1 were obtained for WG versus baseline.

SIGNIFICANCE

The proposed analysis techniques significantly improved the hybrid BCI performance. Specifically, for MR/WG versus baseline problems, we achieved twice of the ITRs obtained in our previous study. Moreover, the ITR of WG versus MR problem is 4-times the ITR we obtained before for the same problem. The current analysis methods boosted the performance of our EEG-fTCD BCI such that it outperformed the existing EEG-fNIRS BCIs in comparison.

Classification of Movement Intention Using Independent Components of Premovement EEG

Many previous studies on brain-machine interfaces (BMIs) have focused on electroencephalography (EEG) signals elicited during motor-command execution to generate device commands. However, exploiting pre-execution brain activity related to movement intention could improve the practical applicability of BMIs. Therefore, in this study we investigated whether EEG signals occurring before movement execution could be used to classify movement intention. Six subjects performed reaching tasks that required them to move a cursor to one of four targets distributed horizontally and vertically from the center. Using independent components of EEG acquired during a premovement phase, two-class classifications were performed for left vs. right trials and top vs. bottom trials using a support vector machine. Instructions were presented visually (test) and aurally (condition). In the test condition, accuracy for a single window was about 75%, and it increased to 85% in classification using two windows. In the control condition, accuracy for a single window was about 73%, and it increased to 80% in classification using two windows. Classification results showed that a combination of two windows from different time intervals during the premovement phase improved classification performance in the both conditions compared to a single window classification. By categorizing the independent components according to spatial pattern, we found that information depending on the modality can improve classification performance. We confirmed that EEG signals occurring during movement preparation can be used to control a BMI.

A Systemic Review of Functional Near-Infrared Spectroscopy for Stroke: Current Application and Future Directions

Background: Survivors of stroke often experience significant disability and impaired quality of life. The recovery of motor or cognitive function requires long periods. Neuroimaging could measure changes in the brain and monitor recovery process in order to offer timely treatment and assess the effects of therapy. A non-invasive neuroimaging technique near-infrared spectroscopy (NIRS) with its ambulatory, portable, low-cost nature without fixation of subjects has attracted extensive attention. Methods: We conducted a comprehensive literature review in order to review the use of NIRS in stroke or post-stroke patients in July 2018. NCBI Pubmed database, EMBASE database, Cochrane Library and ScienceDirect database were searched. Results: Overall, we reviewed 66 papers. NIRS has a wide range of application, including in monitoring upper limb, lower limb recovery, motor learning, cortical function recovery, cerebral hemodynamic changes, cerebral oxygenation, as well as in therapeutic method, clinical researches, and evaluation of the risk for stroke. Conclusions: This study provides a preliminary evidence of the application of NIRS in stroke patients as a monitoring, therapeutic, and research tool. Further studies could give more emphasize on the combination of NIRS with other techniques and its utility in the prevention of stroke.

Early Detection of Hemodynamic Responses Using EEG: A Hybrid EEG-fNIRS Study

Enhanced classification accuracy and a sufficient number of commands are highly demanding in brain computer interfaces (BCIs). For a successful BCI, early detection of brain commands in time is essential. In this paper, we propose a novel classifier using a modified vector phase diagram and the power of electroencephalography (EEG) signal for early prediction of hemodynamic responses. EEG and functional near-infrared spectroscopy (fNIRS) signals for a motor task (thumb tapping) were obtained concurrently. Upon the resting state threshold circle in the vector phase diagram that uses the maximum values of oxy- and deoxy-hemoglobin (ΔHbO and ΔHbR) during the resting state, we introduce a secondary (inner) threshold circle using the ΔHbO and ΔHbR magnitudes during the time window of 1 s where an EEG activity is noticeable. If the trajectory of ΔHbO and ΔHbR touches the resting state threshold circle after passing through the inner circle, this indicates that ΔHbO was increasing and ΔHbR was decreasing (i.e., the start of a hemodynamic response). It takes about 0.5 s for an fNIRS signal to cross the resting state threshold circle after crossing the EEG-based circle. Thus, an fNIRS-based BCI command can be generated in 1.5 s. We achieved an improved accuracy of 86.0% using the proposed method in comparison with the 63.8% accuracy obtained using linear discriminant analysis in a window of 0~1.5 s. Moreover, the active brain locations (identified using the proposed scheme) were spatially specific when a t-map was made after 10 s of stimulation. These results demonstrate the possibility of enhancing the classification accuracy for a brain-computer interface with a time window of 1.5 s using the proposed method.

Early Detection of Alzheimer's Disease Using Non-invasive Near-Infrared Spectroscopy

Mild cognitive impairment (MCI) is a cognitive disorder characterized by memory impairment, wherein patients have an increased likelihood of developing Alzheimer’s disease (AD). The classification of MCI and different AD stages is therefore fundamental for understanding and treating the disease. This study aimed to comprehensively investigate the hemodynamic response patterns among various subject groups. Functional near-infrared spectroscopy (fNIRS) was employed to measure signals from the frontal and bilateral parietal cortices of healthy controls (n = 8), patients with MCI (n = 9), mild (n = 6), and moderate/severe AD (n = 7) during a digit verbal span task (DVST). The concentration changes of oxygenated hemoglobin (HbO) in various subject groups were thoroughly explored and tested. Result revealed that abnormal patterns of hemodynamic response were observed across all subject groups. Greater and steeper reductions in HbO concentration were consistently observed across all regions of interest (ROIs) as disease severity developed from MCI to moderate/severe AD. Furthermore, all the fNIRS-derived indexes were found to be significantly and positively correlated to the clinical scores in all ROIs (R ≥ 0.4, P < 0.05). These findings demonstrate the feasibility of utilizing fNIRS for the early detection of AD, suggesting that fNIRS-based approaches hold great promise for exploring the mechanisms underlying the progression of AD.

Hybrid System for Engagement Recognition During Cognitive Tasks Using a CFS + KNN Algorithm

Engagement is described as a state in which an individual involved in an activity can ignore other influences. The engagement level is important to obtaining good performance especially under study conditions. Numerous methods using electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS) for the recognition of engagement have been proposed. However, the results were either unsatisfactory or required many channels. In this study, we introduce the implementation of a low-density hybrid system for engagement recognition. We used a two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS to measure engagement recognition during cognitive tasks. We used electrooculograms (EOG) and eye tracking to record eye movements for data labeling. We calculated the recognition accuracy using the combination of correlation-based feature selection and k-nearest neighbor algorithm. Following that, we did a comparative study against a stand-alone system. The results show that the hybrid system had an acceptable accuracy for practical use (71.65 ± 0.16%). In comparison, the accuracy of a pure EEG system was (65.73 ± 0.17%), pure ECG (67.44 ± 0.19%), and pure NIRS (66.83 ± 0.17%). Overall, our results demonstrate that the proposed method can be used to improve performance in engagement recognition.

Existence of Initial Dip for BCI: An Illusion or Reality

A tight coupling between the neuronal activity and the cerebral blood flow (CBF) is the motivation of many hemodynamic response (HR)-based neuroimaging modalities. The increase in neuronal activity causes the increase in CBF that is indirectly measured by HR modalities. Upon functional stimulation, the HR is mainly categorized in three durations: (i) initial dip, (ii) conventional HR (i.e., positive increase in HR caused by an increase in the CBF), and (iii) undershoot. The initial dip is a change in oxygenation prior to any subsequent increase in CBF and spatially more specific to the site of neuronal activity. Despite additional evidence from various HR modalities on the presence of initial dip in human and animal species (i.e., cat, rat, and monkey); the existence/occurrence of an initial dip in HR is still under debate. This article reviews the existence and elusive nature of the initial dip duration of HR in intrinsic signal optical imaging (ISOI), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The advent of initial dip and its elusiveness factors in ISOI and fMRI studies are briefly discussed. Furthermore, the detection of initial dip and its role in brain-computer interface using fNIRS is examined in detail. The best possible application for the initial dip utilization and its future implications using fNIRS are provided.