A 12-Channel, real-time near-infrared spectroscopy instrument for brain-computer interface applications

A continuous wave near-infrared spectroscopy (NIRS) instrument for brain-computer interface (BCI) applications is presented. In the literature, experiments have been carried out on subjects with such motor degenerative diseases as amyotrophic lateral sclerosis, which have demonstrated the suitability of NIRS to access intentional functional activity, which could be used in a BCI as a communication aid. Specifically, a real-time, multiple channel NIRS tool is needed to realise access to even a few different mental states, for reasonable baud rates. The 12-channel instrument described here has a spatial resolution of 30mm, employing a flexible software demodulation scheme. Temporal resolution of ~100ms is maintained since typical topographic imaging is not needed, since we are only interested in exploiting the vascular response for BCI control. A simple experiment demonstrates the ability of the system to report on haemodynamics during single trial mental arithmetic tasks. Multiple trial averaging is not required

Can time-resolved NIRS provide the sensitivity to detect brain activity during motor imagery consistently?

Previous functional magnetic resonance imaging (fMRI) studies have shown that a subgroup of patients diagnosed as being in a vegetative state are aware and able to communicate by performing a motor imagery task in response to commands. Due to the fMRI\u27s cost and accessibility, there is a need for exploring different imaging modalities that can be used at the bedside. A promising technique is functional near infrared spectroscopy (fNIRS) that has been successfully applied to measure brain oxygenation in humans. Due to the limited depth sensitivity of continuous-wave NIRS, time-resolved (TR) detection has been proposed as a way of enhancing the sensitivity to the brain, since late arriving photons have a higher probability of reaching the brain. The goal of this study was to assess the feasibility and sensitivity of TR fNIRS in detecting brain activity during motor imagery. Fifteen healthy subjects were recruited in this study, and the fNIRS results were validated using fMRI. The change in the statistical moments of the distribution of times of flight (number of photons, mean time of flight and variance) were calculated for each channel to determine the presence of brain activity. The results indicate up to an 86% agreement between fMRI and TR-fNIRS and the sensitivity ranging from 64 to 93% with the highest value determined for the mean time of flight. These promising results highlight the potential of TR-fNIRS as a portable brain computer interface for patients with disorder of consciousness

Classification of Overt and Covert Speech for Near-Infrared Spectroscopy-Based Brain Computer Interface

Published: 7 September 2018People suffering from neuromuscular disorders such as locked-in syndrome (LIS) are left in a paralyzed state with preserved awareness and cognition. In this study, it was hypothesized that changes in local hemodynamic activity, due to the activation of Broca’s area during overt/covert speech, can be harnessed to create an intuitive Brain Computer Interface based on Near-Infrared Spectroscopy (NIRS). A 12-channel square template was used to cover inferior frontal gyrus and changes in hemoglobin concentration corresponding to six aloud (overtly) and six silently (covertly) spoken words were collected from eight healthy participants. An unsupervised feature extraction algorithm was implemented with an optimized support vector machine for classification. For all participants, when considering overt and covert classes regardless of words, classification accuracy of 92.88 18.49% was achieved with oxy-hemoglobin (O2Hb) and 95.14 5.39% with deoxy-hemoglobin (HHb) as a chromophore. For a six-active-class problem of overtly spoken words, 88.19 7.12% accuracy was achieved for O2Hb and 78.82 15.76% for HHb. Similarly, for a six-active-class classification of covertly spoken words, 79.17 14.30% accuracy was achieved with O2Hb and 86.81 9.90% with HHb as an absorber. These results indicate that a control paradigm based on covert speech can be reliably implemented into future Brain–Computer Interfaces (BCIs) based on NIRSThis research received no external funding

Development of A Versatile Multichannel CWNIRS Instrument for Optical Brain-Computer Interface Applications

This thesis describes the design, development, and implementation of a versatile multichannel continuous-wave near-infrared spectroscopy (CWNIRS) instrument for brain-computer interface (BCI) applications. Specifically, it was of interest to assess what gains could be achieved by using a multichannel device compared to the single channel device implemented by Coyle in 2004. Moreover, the multichannel approach allows for the assessment of localisation of functional tasks in the cerebral cortex, and can identify lateralisation of haemodynamic responses to motor events. The approach taken to extend single channel to multichannel was based on a software-controlled interface. This interface allowed flexibility in the control of individual optodes including their synchronisation and modulation (AM, TDM, CDMA). Furthermore, an LED driver was developed for custom-made triple-wavelength LEDs. The system was commissioned using a series of experiments to verify the performance of individual components in the system. The system was then used to carry out a set of functional studies including motor imagery and cognitive tasks. The experimental protocols based on motor imagery and overt motor tasks were verified by comparison with fMRI. The multichannel approach identified stroke rehabilitation as a new application area for optical BCI. In addition, concentration changes in deoxyhaemoglobin were identified as being a more localised indicator of functional activity, which is important for effective BCI design. An assessment was made on the effect of the duration of the stimulus period on the haemodynamic signals. This demonstrated the possible benefits of using a shorter stimulus period to reduce the adverse affects of low blood pressure oscillations. i

Development of A Versatile Multichannel CWNIRS Instrument for Optical Brain-Computer Interface Applications

This thesis describes the design, development, and implementation of a versatile multichannel continuous-wave near-infrared spectroscopy (CWNIRS) instrument for brain-computer interface (BCI) applications. Specifically, it was of interest to assess what gains could be achieved by using a multichannel device compared to the single channel device implemented by Coyle in 2004. Moreover, the multichannel approach allows for the assessment of localisation of functional tasks in the cerebral cortex, and can identify lateralisation of haemodynamic responses to motor events. The approach taken to extend single channel to multichannel was based on a software-controlled interface. This interface allowed flexibility in the control of individual optodes including their synchronisation and modulation (AM, TDM, CDMA). Furthermore, an LED driver was developed for custom-made triple-wavelength LEDs. The system was commissioned using a series of experiments to verify the performance of individual components in the system. The system was then used to carry out a set of functional studies including motor imagery and cognitive tasks. The experimental protocols based on motor imagery and overt motor tasks were verified by comparison with fMRI. The multichannel approach identified stroke rehabilitation as a new application area for optical BCI. In addition, concentration changes in deoxyhaemoglobin were identified as being a more localised indicator of functional activity, which is important for effective BCI design. An assessment was made on the effect of the duration of the stimulus period on the haemodynamic signals. This demonstrated the possible benefits of using a shorter stimulus period to reduce the adverse affects of low blood pressure oscillations. i

Automatic single-trial discrimination of mental arithmetic, mental singing and the no-control state from prefrontal activity: toward a three-state NIRS-BCI

<p>Abstract</p> <p>Background</p> <p>Near-infrared spectroscopy (NIRS) is an optical imaging technology that has recently been investigated for use in a safe, non-invasive brain-computer interface (BCI) for individuals with severe motor impairments. To date, most NIRS-BCI studies have attempted to discriminate two mental states (e.g., a mental task and rest), which could potentially lead to a two-choice BCI system. In this study, we attempted to automatically differentiate three mental states - specifically, intentional activity due to 1) a mental arithmetic (MA) task and 2) a mental singing (MS) task, and 3) an unconstrained, "no-control (NC)" state - to investigate the feasibility of a three-choice system-paced NIRS-BCI.</p> <p>Results</p> <p>Deploying a dual-wavelength frequency domain near-infrared spectrometer, we interrogated nine sites around the frontopolar locations while 7 able-bodied adults performed mental arithmetic and mental singing to answer multiple-choice questions within a system-paced paradigm. With a linear classifier trained on a ten-dimensional feature set, an overall classification accuracy of 56.2% was achieved for the MA vs. MS vs. NC classification problem and all individual participant accuracies significantly exceeded chance (i.e., 33%). However, as anticipated based on results of previous work, the three-class discrimination was unsuccessful for three participants due to the ineffectiveness of the mental singing task. Excluding these three participants increases the accuracy rate to 62.5%. Even without training, three of the remaining four participants achieved accuracies approaching 70%, the value often cited as being necessary for effective BCI communication.</p> <p>Conclusions</p> <p>These results are encouraging and demonstrate the potential of a three-state system-paced NIRS-BCI with two intentional control states corresponding to mental arithmetic and mental singing.</p

Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface

We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbour (kNN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that the p values were statistically significant relative to all of the other classifiers (p < 0.005) using HbO signals

Using EEG and NIRS for brain-computer interface and cognitive performance measures: a pilot study

This study addresses two important problem statements, namely, selection of training datasets for online Brain-Computer Interface (BCI) classifier training and determination of participant concentration levels during an experiment. The work also attempted a pilot study to integrate electroencephalograms (EEGs) and Near Infra Red Spectroscopy (NIRS) for possible applications such as the BCI and for measuring cognitive levels. Two experiments are presented, the first being a mathematical task interleaved with rest states using NIRS only. In the next, integration of the EEG-NIRS with reference to P300-based BCI systems as well as the experimental conditions designed to elicit the concentration levels (denoted as ON and OFF states here) during the paradigm, are presented. The first experiment indicates that NIRS can be used to differentiate a concentrated (i.e., mental activity) level from the rest. However, the second experiment reveals statistically significant results using the EEG only. We present details about the equipment used, the participants as well as the signal processing and machine learning techniques implemented to analyse the EEG and NIRS data. After discussing the results, we conclude by describing the research scope as well as the possible pitfalls in this work from a NIRS viewpoint, which presents an opportunity for future research exploration for BCI and cognitive performance measures

Shining a Light on Awareness::A Review of Functional Near-Infrared Spectroscopy for Prolonged Disorders of Consciousness

Qualitative clinical assessments of the recovery of awareness after severe brain injury require an assessor to differentiate purposeful behavior from spontaneous behavior. As many such behaviors are minimal and inconsistent, behavioral assessments are susceptible to diagnostic errors. Advanced neuroimaging tools can bypass behavioral responsiveness and reveal evidence of covert awareness and cognition within the brains of some patients, thus providing a means for more accurate diagnoses, more accurate prognoses, and, in some instances, facilitated communication. The majority of reports to date have employed the neuroimaging methods of functional magnetic resonance imaging, positron emission tomography, and electroencephalography (EEG). However, each neuroimaging method has its own advantages and disadvantages (e.g., signal resolution, accessibility, etc.). Here, we describe a burgeoning technique of non-invasive optical neuroimaging—functional near-infrared spectroscopy (fNIRS)—and review its potential to address the clinical challenges of prolonged disorders of consciousness. We also outline the potential for simultaneous EEG to complement the fNIRS signal and suggest the future directions of research that are required in order to realize its clinical potential