Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system

Background: For patients with disorders of consciousness such as coma, a vegetative state or a minimally conscious state, one challenge is to detect and assess the residual cognitive functions in their brains. Number processing and mental calculation are important brain functions but are difficult to detect in patients with disorders of consciousness using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised due to the patients' motor impairments and inability to provide sufficient motor responses for number- and calculation-based communication. Methods: In this study, we presented a hybrid brain-computer interface that combines P300 and steady state visual evoked potentials to detect number processing and mental calculation in Han Chinese patients with disorders of consciousness. Eleven patients with disorders of consciousness who were in a vegetative state (n = 6) or in a minimally conscious state (n = 3) or who emerged from a minimally conscious state (n = 2) participated in the brain-computer interface-based experiment. During the experiment, the patients with disorders of consciousness were instructed to perform three tasks, i.e., number recognition, number comparison, and mental calculation, including addition and subtraction. In each experimental trial, an arithmetic problem was first presented. Next, two number buttons, only one of which was the correct answer to the problem, flickered at different frequencies to evoke steady state visual evoked potentials, while the frames of the two buttons flashed in a random order to evoke P300 potentials. The patients needed to focus on the target number button (the correct answer). Finally, the brain-computer interface system detected P300 and steady state visual evoked potentials to determine the button to which the patients attended, further presenting the results as feedback. Results: Two of the six patients who were in a vegetative state, one of the three patients who were in a minimally conscious state, and the two patients that emerged from a minimally conscious state achieved accuracies significantly greater than the chance level. Furthermore, P300 potentials and steady state visual evoked potentials were observed in the electroencephalography signals from the five patients. Conclusions: Number processing and arithmetic abilities as well as command following were demonstrated in the five patients. Furthermore, our results suggested that through brain-computer interface systems, many cognitive experiments may be conducted in patients with disorders of consciousness, although they cannot provide sufficient behavioral responses. © 2015 Li et al

Functional Magnetic Resonance Imaging as an Assessment Tool in Critically Ill Patients

Little is known about whether residual cognitive function occurs in the earliest stages of brain injury. The overarching goal of the work presented in this dissertation was to elucidate the role of functional neuroimaging in assessing brain activity in critically ill patients. The overall objective was addressed in the following four empirical chapters: In Chapter 2, three versions of a hierarchically-designed auditory task were developed and their ability to detect various levels of auditory language processing was assessed in individual healthy participants. The same procedure was then applied in two acutely comatose patients. In Chapter 3, a hierarchical auditory task was employed in a heterogeneous cohort of acutely comatose patients. The results revealed that the level of auditory processing in coma may be predictive of subsequent functional recovery. In Chapter 4, two mental imagery paradigms were utilized to assess covert command-following in coma. The findings demonstrate, for the first time, preserved awareness in an acutely comatose patient. In Chapter 5, functional neuroimaging techniques were used for covert communication with two completely locked-in, critically ill patients. The results suggest that this methodology could be used as an augmentative communication tool to allow patients to be involved in their own medical decision-making. Taken together, the proceeding chapters of this work demonstrate that functional neuroimaging can detect preserved cognitive functions in some acutely comatose patients, which has both diagnostic and prognostic relevance. Moreover, these techniques may be extended even further to be used as a communication tool in critically ill patients

Analysis of consciousness for complete locked-in syndrome patients

This thesis presents methods for detecting consciousness in patients with complete locked-in syndrome (CLIS). CLIS patients are unable to speak and have lost all muscle movement. Externally, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to be still conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is vital to develop alternative ways to re-establish communication with these patients during periods of awareness, and a possible platform is through brain–computer interface (BCI). Since consciousness is required to use BCI correctly, this study proposes a modus operandi to analyze not only in intracranial electrocorticography (ECoG) signals with greater signal-to-noise ratio (SNR) and higher signal amplitude, but also in non-invasive electroencephalography (EEG) signals. By applying three different time-domain analysis approaches sample entropy, permutation entropy, and Poincaré plot as feature extraction to prevent disease-related reductions of brainwave frequency bands in CLIS patients, and cross-validated to improve the probability of correctly detecting the conscious states of CLIS patients. Due to the lack a of 'ground truth' that could be used as teaching input to correct the outcomes, k-Means and DBSCAN these unsupervised learning methods were used to reveal the presence of different levels of consciousness for individual participation in the experiment first in locked-in state (LIS) patients with ALSFRS-R score of 0. The results of these different methods converge on the specific periods of consciousness of CLIS/LIS patients, coinciding with the period during which CLIS/LIS patients recorded communication with an experimenter. To determine methodological feasibility, the methods were also applied to patients with disorders of consciousness (DOC). The results indicate that the use of sample entropy might be helpful to detect awareness not only in CLIS/LIS patients but also in minimally conscious state (MCS)/unresponsive wakefulness syndrome (UWS) patients, and showed good resolution for both ECoG signals up to 24 hours a day and EEG signals focused on one or two hours at the time of the experiment. This thesis focus on consistent results across multiple channels to avoid compensatory effects of brain injury. Unlike most techniques designed to help clinicians diagnose and understand patients' long-term disease progression or distinguish between different disease types on the clinical scales of consciousness. The aim of this investigation is to develop a reliable brain-computer interface-based communication aid eventually to provide family members with a method for short-term communication with CLIS patients in daily life, and at the same time, this will keep patients' brains active to increase patients' willingness to live and improve their quality of life (QOL)

The N400 for Brain Computer Interfacing: complexities and opportunities

The N400 is an Event Related Potential that is evoked in response to conceptually meaningful stimuli. It is for instance more negative in response to incongruent than congruent words in a sentence, and more negative for unrelated than related words following a prime word. This sensitivity to semantic content of a stimulus in relation to the mental context of an individual makes it a signal of interest for Brain Computer Interfaces. Given this potential it is notable that the BCI literature exploiting the N400 is limited. We identify three existing application areas: (1) exploiting the semantic processing of faces to enhance matrix speller performance, (2) detecting language processing in patients with Disorders of Consciousness, and (3) using semantic stimuli to probe what is on a user's mind. Drawing on studies from these application areas, we illustrate that the N400 can successfully be exploited for BCI purposes, but that the signal-to-noise ratio is a limiting factor, with signal strength also varying strongly across subjects. Furthermore, we put findings in context of the general N400 literature, noting open questions and identifying opportunities for further research.Comment: 28 pages, 2 figures, 2 table

Longitudinal ALS study: research of EEG biomarkers during the progression of the disease

ALS is a neurodegenerative disorder that brings patients to a state of complete paralysis. In this thesis a longitudinal analysis of EEG resting state data is performed for three patients, setting a signal processing pipeline to detect which features of the signal are changing over the observation period. It has been found a substantial difference in the spectral content of EEG signal between late-stage patients and the one observed during the transition to completely locked-in state (CLIS)

A cross-subject decoding algorithm for patients with disorder of consciousness based on P300 brain computer interface

BackgroundBrain computer interface (BCI) technology may provide a new way of communication for some patients with disorder of consciousness (DOC), which can directly connect the brain and external devices. However, the DOC patients’ EEG differ significantly from that of the normal person and are difficult to collected, the decoding algorithm currently only is trained based on a small amount of the patient’s own data and performs poorly.MethodsIn this study, a decoding algorithm called WD-ADSTCN based on domain adaptation is proposed to improve the DOC patients’ P300 signal detection. We used the Wasserstein distance to filter the normal population data to increase the training data. Furthermore, an adversarial approach is adopted to resolve the differences between the normal and patient data.ResultsThe results showed that in the cross-subject P300 detection of DOC patients, 7 of 11 patients achieved an average accuracy of over 70%. Furthermore, their clinical diagnosis changed and CRS-R scores improved three months after the experiment.ConclusionThese results demonstrated that the proposed method could be employed in the P300 BCI system for the DOC patients, which has important implications for the clinical diagnosis and prognosis of these patients

Consciousness level assessment in completely locked-in syndrome patients using soft-clustering

Brain-computer interfaces (BCIs) are very convenient tools to assess locked-in (LIS) and completely locked-in state (CLIS) patients' hidden states of consciousness. For the time being, there is no ground-truth data in respect to these states for above-mentioned patients. This lack of gold standard makes this problem particularly challenging. In addition to consciousness assessment, BCIs also provide them with a communication device that does not require the presence of motor responses, which they are lacking. Communication plays an important role in the patients' quality of life and prognosis. Significant progress have been made to provide them with EEG-based BCIs in particular. Nonetheless, the majority of existing studies directly dive into the communication part without assessing if the patient is even conscious. Additionally, the few studies that do essentially use evoked brain potentials, mostly the P300, that necessitates the patient's voluntary and active participation to be elicited. Patients are easily fatigued, and would consequently be less successful during the main communication task. Furthermore, when the consciousness states are determined using resting state data, only one or two features were used. In this thesis, different sets of EEG features are used to assess the consciousness level of CLIS patients using resting-state data. This is done as a preliminary step that needed to be succeeded in order to engage to the next step, communication with the patient. In other words, the 'conversation' is initiated only if the patient is sufficiently conscious. This variety of EEG features is utilised to increase the probability of correctly estimating the patients' consciousness states. Indeed, each of them captures a particular signal attribute, and combining them would allow the collection of different hidden characteristics that could have not been obtained from a single feature. Furthermore, the proposed method should allow to determine if communication shall be initiated at a specific time with the patient. The EEG features used are frequency-based, complexity related and connectivity metrics. Besides, instead of analysing results from individual channels or specific brain regions, the global activity of the brain is assessed. The estimated consciousness levels are then obtained by applying two different soft-clustering analysis methods, namely Fuzzy c-means (FCM) and Gaussian Mixture Models (GMM), to the individual features and ensembling their results using their average or their product. The proposed approach is first applied to EEG data recorded from patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) (patients with disorders of consciousness (DoC)) to evaluate its performance. It is subsequently applied to data from one CLIS patient that is unique in its kind because it contains a time frame during which the experimenters affirmed that he was conscious. Finally, it is used to estimate the levels of consciousness of nine other CLIS patients. The obtained results revealed that the presented approach was able to take into account the variations of the different features and deduce a unique output taking into consideration the individual features contributions. Some of them performed better than others, which is not surprising since each person is different. It was also able to draw very accurate estimations of the level of consciousness under specific conditions. The approach presented in this thesis provides an additional tool for diagnosis to the medical staff. Furthermore, when implemented online, it would enable to determine the optimal time to engage in communication with CLIS patients. Moreover, it could possibly be used to predict patients' cognitive decline and/or death

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

COGNITIVE PROCESSING AND BRAIN COMMUNICATION IN AMYOTROPHIC LATERAL SCLEROSIS

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive paralysis of limbs and bulbar musculature. This severe physical impairment makes cognitive evaluation a big challenge, thus there is a great need for an assessment that does not require overt motor responses. Moreover, we need of augmentative communication strategies because the disease generally leads to complete paralysis and, therefore, patients are unable to communicate with the external world by any means. For this purpose, Brain Computer Interfaces (BCIs) seem a promising approach to facilitate communication with these patients. The aim of this thesis is twofold. First, assessing cognitive processing in ALS by means of a novel evaluation tool. Second, allowing brain communication in completely paralyzed ALS patients who had lost their vision in order to eliminate the unbearable loss of communication in paralysis (“unlocking the locked-in”). The first study introduces a novel approach for assessing cognitive functions in ALS. This approach uses neuropsychological tests that require minimal overt motor or verbal responses; together with vibro-tactile P300s. Results indicate mild cognitive impairment in oral language comprehension tasks and reduced vibro-tactile P300 amplitudes in patients compared to healthy controls. Importantly, correlations between the vibro-tactile P300 latency and psychometric test results suggest that the former measure could serve as a neurophysiological marker of cognitive decline in ALS patients. The second study introduces a distraction paradigm based in auditory event-related potentials (ERPs) to evaluate the ability of change detection, focusing, and re-orientation of attention in ALS. The results revealed a modification of the amplitude and the latency of the N200, the P300 and the re-orienting negativity (RON) components. This could suggest an alteration of the endogenous mechanism that controls the detection of change, thus resulting in a reduction of the allocation and the re-orientation of attentional resources. The third study aimed at testing the feasibility of a Near Infrared Spectroscopy (NIRS) -based BCI communication approach for patients in the Completely Locked-in Stage (CLIS) due to ALS. For this purpose two CLIS patients were trained to control their cerebral-cortex´s functional-activations in response to auditory processing of correct or incorrect statements assessed with NIRS. The results of the study are very promising, showing that both CLIS patients communicated with fronto-cortical oxygenation based BCI at an average correct response rate of 70% over a period of several weeks. We conclude that this novel approach of brain-communication is safe and, reliable, representing, so far, the best communication possible for patients in completely locked-in state. In conclusion we propose a) the novel combination of vibro-tactile or acoustic ERPs and motor-independent neuropsychological tests as an alternative and easily implementable way for assessing cognitive functions in ALS and b) we confirm the usefulness and effectiveness of above mentioned electrophysiological approaches in the late stage of ALS either to assess cognitive processing or to establish communication with a BCI system