Mental Task Evaluation for Hybrid NIRS-EEG Brain-Computer Interfaces

Based on recent electroencephalography (EEG) and near-infrared spectroscopy (NIRS) studies that showed that tasks such as motor imagery and mental arithmetic induce specific neural response patterns, we propose a hybrid brain-computer interface (hBCI) paradigm in which EEG and NIRS data are fused to improve binary classification performance. We recorded simultaneous NIRS-EEG data from nine participants performing seven mental tasks (word generation, mental rotation, subtraction, singing and navigation, and motor and face imagery). Classifiers were trained for each possible pair of tasks using (1) EEG features alone, (2) NIRS features alone, and (3) EEG and NIRS features combined, to identify the best task pairs and assess the usefulness of a multimodal approach. The NIRS-EEG approach led to an average increase in peak kappa of 0.03 when using features extracted from one-second windows (equivalent to an increase of 1.5% in classification accuracy for balanced classes). The increase was much stronger (0.20, corresponding to an 10% accuracy increase) when focusing on time windows of high NIRS performance. The EEG and NIRS analyses further unveiled relevant brain regions and important feature types. This work provides a basis for future NIRS-EEG hBCI studies aiming to improve classification performance toward more efficient and flexible BCIs

Hybrid head cap for mouse brain studies

Abstract. In this thesis, I present a hybrid head cap in combination with non-invasive multi-channel Electroencephalogram (EEG) and Near-Infrared Spectroscopy (NIRS) to measure brainwaves on mice’s scalps. Laboratory animal research provides insights into multiple potential applications involving humans and other animals. An experimental framework that targets laboratory animals can lead to useful transnational research if it strongly reflects the actual application environment. The non-invasive head cap with three electrodes for EEG and two optodes for NIRS is suggested to measure brainwaves throughout the laboratory mice’s entire brain region without surgical procedures. The suggested hybrid head cap aims to ensure stability in vivo monitoring for mouse brain in a non-invasive way, similarly as the monitoring is performed for the human brain. The experimental part of the work to study the quality of the gathered EEG and fNIRS signals, and usability validation of the head cap, however, was not completed in the planned time frame of the thesis work

An examination of conceptual knowledge using near-infrared spectroscopy and electroencephalography

Traditionally, models of conceptual knowledge have relied upon amodal theories that largely overlook how environmental stimuli are converted into amodal representations and how perceptions reactivate these representations and translate them back into subjective modal experiences. Developed more recently, grounded cognition theories propose that physical experiences and conceptual knowledge rely, at least in part, on the same brain regions. Thus, conceptual knowledge is hypothesized to be experienced through the reactivation of the same brain regions that are activated during physical experiences with the environment. Furthermore, if these grounded hypotheses are correct, researchers should be able to observe predictable influences of grounded information on brain activity as well as participant response latencies and accuracy in experimental conditions. To this end, three experiments were conducted testing these hypotheses using semantic categorization tasks while simultaneous recordings were taken using functional near infrared spectroscopy (NIRS) and electroencephalography. It was hypothesized that the influence of automatically reactivated grounded information would be facilitatory (i.e., resulting in faster and more accurate responses for semantically richer words) when it was task congruent, but would be inhibitory (i.e., resulting in slower and less accurate responses for semantically richer words) when it was task incongruent, thus illustrating the automatic simulation of grounded information in the processing and retrieval of conceptual knowledge. NIRS was employed to monitor event-related patterns of prefrontal cortex (PFC) hemodynamics associated with these tasks. It was hypothesized that trials with high levels of task-relevant semantic information would be discernably different than those with low levels or those trials high in task-incongruent information. That is, given the high levels of task-relevant semantic information, these trials should be comparatively easier, thus requiring less activity in the PFC, resulting in less pronounced hemodynamic responses. Electroencephalography was employed to monitor the full-scalp event-related patterns of brain activity associated with the experimental tasks. It was hypothesized that event-related potential deflections and scalp topography would be able to discern qualitatively and quantitatively different patterns of activity as a function of the amount and relevance of grounded information obtained through physical and emotional experiences with the word stimuli’s referents. The behavioural, accuracy, and electroencephalography data generally support these hypotheses. When a stimulus’s grounded information is high and task relevant, participants responded more quickly and accurately, and had discernably different patterns of brain activity than when a stimulus’s grounded information was low and task relevant. When a stimulus’s grounded information was high and task-irrelevant, participants were slower and less accurate, and exhibited patterns of brain activity that reflected both the additional semantic information and the additional processing necessary to reconcile the task incongruence. Unfortunately, data obtained from NIRS failed to illustrate meaningful condition differences. Possible reasons for this are discussed in detail in Chapter 3. Collectively, the data presented in this dissertation serve to advance and extend the claims made by grounded cognition theorists by illustrating the automatic simulation of information obtained through interactions with the environment. Further research is required to extend this work to other brain regions and to develop NIRS methods that can address these research questions

Towards simultaneous electroencephalography and functional near-infrared spectroscopy for improving diagnostic accuracy in prolonged disorders of consciousness: a healthy cohort study

Qualitative clinical assessments of the recovery of awareness after severe brain injury require an assessor to differentiate purposeful behaviour from spontaneous behaviour. As many such behaviours are minimal and inconsistent, behavioural assessments are susceptible to diagnostic errors. Advanced neuroimaging tools such as functional magnetic resonance imaging and electroencephalography (EEG) can bypass behavioural 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. As each individual neuroimaging method has its own advantages and disadvantages (e.g., signal resolution, accessibility, etc.), this thesis studies on healthy individuals a burgeoning technique of non-invasive electrical and optical neuroimaging—simultaneous EEG and functional near-infrared spectroscopy (fNIRS)—that can be applied at the bedside. Measuring reliable covert behaviours is correlated with participant engagement, instrumental sensitivity and the accurate localisation of responses, aspects which are further addressed over three studies. Experiment 1 quantifies the typical EEG changes in response to covert commands in the absence and presence of an object. This is investigated to determine whether a goal-directed task can yield greater EEG control accuracy over simple monotonous imagined single-joint actions. Experiment 2 characterises frequency domain NIRS changes in response to overt and covert hand movements. A method for reconstructing haemodynamics using the less frequently investigated phase parameter is outlined and the impact of noise contaminated NIRS measurements are discussed. Furthermore, classification performances between frequency-domain and continuous-wave-like signals are compared. Experiment 3 lastly applies these techniques to determine the potential of simultaneous EEG-fNIRS classification. Here a sparse channel montage that would ultimately favour clinical utility is used to demonstrate whether such a hybrid method containing rich spatial and temporal information can improve the classification of covert responses in comparison to unimodal classification of signals. The findings and discussions presented within this thesis identify a direction for future research in order to more accurately translate the brain state of patients with a prolonged disorder of consciousness