EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research

A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection

Recently, fuzzy dispersion entropy (DispEn) has attracted much attention as a new nonlinear dynamics method that combines the advantages of both DispEn and fuzzy entropy. However, it suffers from limitation of insensitivity to dynamic changes. To solve this limitation, we proposed fractional fuzzy dispersion entropy (FFDispEn) based on DispEn, a novel fuzzy membership function and fractional calculus. The fuzzy membership function was defined based on the Euclidean distance between the embedding vector and dispersion pattern. Simulated signals generated by the one-dimensional (1D) logistic map were used to test the sensitivity of the proposed method to dynamic changes. Moreover, 29 subjects were recruited for an upper limb muscle fatigue experiment, during which surface electromyography (sEMG) signals of the biceps brachii muscle were recorded. Both simulated signals and sEMG signals were processed using a sliding window approach. Sample entropy (SampEn), DispEn and FFDispEn were separately used to calculate the complexity of each frame. The sensitivity of different algorithms to the muscle fatigue process was analyzed using fitting parameters through linear fitting of the complexity of each frame signal. The results showed that for simulated signals, the larger the fractional order q, the higher the sensitivity to dynamic changes. Moreover, DispEn performed poorly in the sensitivity to dynamic changes compared with FFDispEn. As for muscle fatigue detection, the FFDispEn value showed a clear declining tendency with a mean slope of −1.658 × 10−3 as muscle fatigue progresses; additionally, it was more sensitive to muscle fatigue compared with SampEn (slope: −0.4156 × 10−3) and DispEn (slope: −0.1675 × 10−3). The highest accuracy of 97.5% was achieved with the FFDispEn and support vector machine (SVM). This study provided a new useful nonlinear dynamic indicator for sEMG signal processing and muscle fatigue analysis. The proposed method may be useful for physiological and biomedical signal analysis

EEG Fractal Analysis Reflects Brain Impairment after Stroke

Stroke is the commonest cause of disability. Novel treatments require an improved understanding of the underlying mechanisms of recovery. Fractal approaches have demonstrated that a single metric can describe the complexity of seemingly random fluctuations of physiological signals. We hypothesize that fractal algorithms applied to electroencephalographic (EEG) signals may track brain impairment after stroke. Sixteen stroke survivors were studied in the hyperacute (<48 h) and in the acute phase (∼1 week after stroke), and 35 stroke survivors during the early subacute phase (from 8 days to 32 days and after ∼2 months after stroke): We compared resting-state EEG fractal changes using fractal measures (i.e., Higuchi Index, Tortuosity) with 11 healthy controls. Both Higuchi index and Tortuosity values were significantly lower after a stroke throughout the acute and early subacute stage compared to healthy subjects, reflecting a brain activity which is significantly less complex. These indices may be promising metrics to track behavioral changes in the very early stage after stroke. Our findings might contribute to the neurorehabilitation quest in identifying reliable biomarkers for a better tailoring of rehabilitation pathways

A Hierarchical Meta-model for Multi-Class Mental Task Based Brain-Computer Interfaces

In the last few years, many research works have been suggested on Brain- Computer Interface (BCI), which assists severely physically disabled persons to communicate directly with the help of electroencephalogram (EEG) signal, generated by the thought process of the brain. Thought generation inside the brain is a dynamic process, and plenty thoughts occur within a small time window. Thus, there is a need for a BCI device that can distinguish these various ideas simultaneously. In this research work, our previous binary-class mental task classication has been extended to the multi-class mental task problem. The present work proposed a novel feature construction scheme for multi mental task classication. In the proposed method, features ar

Bypassing the Natural Visual-Motor Pathway to Execute Complex Movement Related Tasks Using Interval Type-2 Fuzzy Sets

In visual-motor coordination, the human brain processes visual stimuli representative of complex motion-related tasks at the occipital lobe to generate the necessary neuronal signals for the parietal and pre-frontal lobes, which in turn generates movement related plans to excite the motor cortex to execute the actual tasks. The paper introduces a novel approach to provide rehabilitative support to patients suffering from neurological damage in their pre-frontal, parietal and/or motor cortex regions. An attempt to bypass the natural visual-motor pathway is undertaken using interval type-2 fuzzy sets to generate the approximate EEG response of the damaged pre-frontal/parietal/motor cortex from the occipital EEG signals. The approximate EEG response is used to trigger a pre-trained joint coordinate generator to obtain desired joint coordinates of the link end-points of a robot imitating the human subject. The robot arm is here employed as a rehabilitative aid in order to move each link end-points to the desired locations in the reference coordinate system by appropriately activating its links using the well-known inverse kinematics approach. The mean-square positional errors obtained for each link end-points is found within acceptable limits for all experimental subjects including subjects with partial parietal damage, indicating a possible impact of the proposed approach in rehabilitative robotics. Subjective variation in EEG features over different sessions of experimental trials is modelled here using interval type-2 fuzzy sets for its inherent power to handle uncertainty. Experiments undertaken confirm that interval type-2 fuzzy realization outperforms its classical type-1 counterpart and back-propagation neural approaches in all experimental cases, considering link positional error as a metric. The proposed research offers a new opening for the development of possible rehabilitative aids for people with partial impairment in visual-motor coordination

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis : Principles and Recent Advances

This work was supported in part by the National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT) under Grant NRF 2020R1A2B5B02002478, and in part by Sejong University through its Faculty Research Program under Grant 20212023.Peer reviewedPublisher PD

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

Multiscale entropy analysis of heart rate variability in neonatal patients with and without seizures

The complex physiological dynamics of neonatal seizures make their detection challenging. A timely diagnosis and treatment, especially in intensive care units, are essential for a better prognosis and the mitigation of possible adverse effects on the newborn’s neurodevelopment. In the literature, several electroencephalographic (EEG) studies have been proposed for a parametric characterization of seizures or their detection by artificial intelligence techniques. At the same time, other sources than EEG, such as electrocardiography, have been investigated to evaluate the possible impact of neonatal seizures on the cardio-regulatory system. Heart rate variability (HRV) analysis is attracting great interest as a valuable tool in newborns applications, especially where EEG technologies are not easily available. This study investigated whether multiscale HRV entropy indexes could detect abnormal heart rate dynamics in newborns with seizures, especially during ictal events. Furthermore, entropy measures were analyzed to discriminate between newborns with seizures and seizure-free ones. A cohort of 52 patients (33 with seizures) from the Helsinki University Hospital public dataset has been evaluated. Multiscale sample and fuzzy entropy showed significant differences between the two groups (p-value < 0.05, Bonferroni multiple-comparison post hoc correction). Moreover, interictal activity showed significant differences between seizure and seizure-free patients (Mann-Whitney Test: p-value < 0.05). Therefore, our findings suggest that HRV multiscale entropy analysis could be a valuable pre-screening tool for the timely detection of seizure events in newborns