A Brain-Computer Interface Based on Three-Dimensional Stereo Stimuli for Assisting Clinical Object Recognition Assessment in Patients With Disorders of Consciousness

Imagined speech classification exploiting EEG power spectrum features

Imagined speech recognition has developed as a significant topic of research in the field of brain-computer interfaces. This innovative technique has great promise as a communication tool, providing essential help to those with impairments. An imagined speech recognition model is proposed in this paper to identify the ten most frequently used English alphabets (e.g., A, D, E, H, I, N, O, R, S, T) and numerals (e.g., 0 to 9). A novel electroencephalogram (EEG) dataset was created by measuring the brain activity of 30 people while they imagined these alphabets and digits. As part of signal preprocessing, EEG signals are filtered before extracting delta, theta, alpha, and beta band power features. These features are used as input for classification using support vector machines, k-nearest neighbors, and random forest (RF) classifiers. It is identified that the RF classifier outperformed the others in terms of classification accuracy. Classification accuracies of 99.38% and 95.39% were achieved at the coarse-level and fine-level, respectively with the RF classifier. From our study, it is also revealed that the beta frequency band and the frontal lobe of the brain played crucial roles in imagined speech recognition. Furthermore, a comparative analysis against state-of-the-art techniques is conducted to demonstrate the efficacy of our proposed model.

Technological Modalities in the Assessment and Treatment of Disorders of Consciousness

Over the last 10 years, there have been rapid advances made in technologies that can be utilized in the diagnosis and treatment of patients with a disorder of consciousness (DoC). This article provides a comprehensive review of these modalities including the evidence supporting their potential use in DoC. This review specifically addresses diagnostic, non-invasive therapeutic, and invasive therapeutic technological modalities except for neuroimaging, which is discussed in another article. While technologic advances appear promising for both assessment and treatment of patients with a DoC, high-quality evidence supporting widespread clinical adoption remains limited.

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

Background

Brain 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.

Methods

In 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.

Results

The 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.

Conclusion

These 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.

EEG-based Brain-Computer Interfaces for people with Disorders of Consciousness: Features and applications. A systematic review

Background

Disorders of Consciousness (DoC) are clinical conditions following a severe acquired brain injury (ABI) characterized by absent or reduced awareness, known as coma, Vegetative State (VS)/Unresponsive Wakefulness Syndrome (VS/UWS), and Minimally Conscious State (MCS). Misdiagnosis rate between VS/UWS and MCS is attested around 40% due to the clinical and behavioral fluctuations of the patients during bedside consciousness assessments. Given the large body of evidence that some patients with DoC possess "covert" awareness, revealed by neuroimaging and neurophysiological techniques, they are candidates for intervention with brain-computer interfaces (BCIs).

Objectives

The aims of the present work are (i) to describe the characteristics of BCI systems based on electroencephalography (EEG) performed on DoC patients, in terms of control signals adopted to control the system, characteristics of the paradigm implemented, classification algorithms and applications (ii) to evaluate the performance of DoC patients with BCI.

Methods

The search was conducted on Pubmed, Web of Science, Scopus and Google Scholar. The PRISMA guidelines were followed in order to collect papers published in english, testing a BCI and including at least one DoC patient.

Results

Among the 527 papers identified with the first run of the search, 27 papers were included in the systematic review. Characteristics of the sample of participants, behavioral assessment, control signals employed to control the BCI, the classification algorithms, the characteristics of the paradigm, the applications and performance of BCI were the data extracted from the study. Control signals employed to operate the BCI were: P300 (N = 19), P300 and Steady-State Visual Evoked Potentials (SSVEP; hybrid system, N = 4), sensorimotor rhythms (SMRs; N = 5) and brain rhythms elicited by an emotional task (N = 1), while assessment, communication, prognosis, and rehabilitation were the possible applications of BCI in DoC patients.

Conclusion

Despite the BCI is a promising tool in the management of DoC patients, supporting diagnosis and prognosis evaluation, results are still preliminary, and no definitive conclusions may be drawn; even though neurophysiological methods, such as BCI, are more sensitive to covert cognition, it is suggested to adopt a multimodal approach and a repeated assessment strategy.

Towards Assessment of Sound Localization in Disorders of Consciousness Using a Hybrid Audiovisual Brain-Computer Interface

Behavioral assessment of sound localization in the Coma Recovery Scale-Revised (CRS-R) poses a significant challenge due to motor disability in patients with disorders of consciousness (DOC). Brain-computer interfaces (BCIs), which can directly detect brain activities related to external stimuli, may thus provide an approach to assess DOC patients without the need for any physical behavior. In this study, a novel audiovisual BCI system was developed to simulate sound localization evaluation in CRS-R. Specifically, there were two alternatively flashed buttons on the left and right sides of the graphical user interface, one of which was randomly chosen as the target. The auditory stimuli of bell sounds were simultaneously presented by the ipsilateral loudspeaker during the flashing of the target button, which prompted patients to selectively attend to the target button. The recorded electroencephalography data were analyzed in real time to detect event-related potentials evoked by the target and further to determine whether the target was attended to or not. A significant BCI accuracy for a patient implied that he/she had sound localization. Among eighteen patients, eleven and four showed sound localization in the BCI and CRS-R, respectively. Furthermore, all patients showing sound localization in the CRS-R were among those detected by our BCI. The other seven patients who had no sound localization behavior in CRS-R were identified by the BCI assessment, and three of them showed improvements in the second CRS-R assessment after the BCI experiment. Thus, the proposed BCI system is promising for assisting the assessment of sound localization and improving the clinical diagnosis of DOC patients.

Application of fast perturbational complexity index to the diagnosis and prognosis for disorders of consciousness

OBJECTIVE

Diagnosis and prognosis of patients with disorders of consciousness (DOC) is a challenge for neuroscience and clinical practice. Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) is an effective tool to measure the level of consciousness. However, a scientific and accurate method to quantify TMS-evoked activity is still lacking. This study applied fast perturbational complexity index (PCIst) to the diagnosis and prognosis of DOC patients.

METHODS

TMS-EEG data of 30 normal healthy participants (NOR) and 181 DOC patients were collected. The PCIst was used to assess the time-space complexity of TMS-evoked potentials (TEP). We selected parameters of PCIst in terms of data length, data delay, sampling rate and frequency band. In addition, we collected Coma Recovery Scale-Revised (CRS-R) values for 114 DOC patients after one year. Finally, we trained the classification and regression model.

RESULTS

1) PCIst shows the differences among NOR, minimally consciousness state (MCS) and unresponsive wakefulness syndrome (UWS) and has low computational cost. 2) Optimal parameters of data length and delay after TMS are 300ms and 101-300ms. Significant differences of PCIst at 5-8Hz and 9-12Hz bands are found among NOR, MCS and UWS groups. PCIst still works when TEP is down-sampled to 250 Hz. 3) PCIst at 9-12Hz shows the highest performance in diagnosis and prognosis of DOC.

CONCLUSIONS

This study confirms that PCIst can quantify the level of consciousness. PCIst is a potential measure for the diagnosis and prognosis of DOC patients.

Advanced Machine-Learning Methods for Brain-Computer Interfacing

The brain-computer interface (BCI) connects the brain and the external world through an information transmission channel by interpreting the physiological information of the brain during thinking activities. The effective classification of electroencephalogram (EEG) signals is the key to improving the performance of the system. To improve the classification accuracy of EEG signals in the BCI system, the transfer learning algorithm and the improved Common Spatial Pattern (CSP) algorithm are combined to construct a data classification model. Finally, the effectiveness of the proposed algorithm is verified. The results show that in actual and imagined movements, the accuracy of the left- and right-hand movements at different speeds is higher than when the speeds are the same. The proposed Adaptive Composite Common Spatial Pattern (ACCSP) and Self Adaptive Common Spatial Pattern (SACSP) algorithms have good classification effects on 5 subjects, with an average classification accuracy rate of 83.58 percent, which is an increase of 6.96 percent compared with traditional algorithms. When the training sample size is 10, the classification accuracy of the ACCSP algorithm is higher than that of the traditional CSP algorithm. The improved CSP algorithm combined with transfer learning embodies a good classification effect in both ACCSP and SACSP. Especially, the performance of SACSP mode is better. Combining the improved CSP algorithm proposed with the CSP-based transfer learning algorithm can improve the classification accuracy of the BCI classifier.

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain-Computer Interfaces: A Systematic Literature Review

Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain-computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

Brain-Computer Interfaces in Neurorecovery and Neurorehabilitation

Recent advances in brain-computer interface technology to restore and rehabilitate neurologic function aim to enable persons with disabling neurologic conditions to communicate, interact with the environment, and achieve other key activities of daily living and personal goals. Here we evaluate the principles, benefits, challenges, and future directions of brain-computer interfaces in the context of neurorehabilitation. We then explore the clinical translation of these technologies and propose an approach to facilitate implementation of brain-computer interfaces for persons with neurologic disease.

The Self-Face Paradigm Improves the Performance of the P300-Speller System

Objective: Previous studies have shown that the performance of the famous face P300-speller was better than that of the classical row/column flashing P300-speller. Furthermore, in some studies, the brain was more active when responding to one's own face than to a famous face, and a self-face stimulus elicited larger amplitude event-related potentials (ERPs) than did a famous face. Thus, we aimed to study the role of the self-face paradigm on further improving the performance of the P300-speller system with the famous face P300-speller paradigm as the control paradigm.

Methods: We designed two facial P300-speller paradigms based on the self-face and a famous face (Ming Yao, a sports star; the famous face spelling paradigm) with a neutral expression.

Results: ERP amplitudes were significantly greater in the self-face than in the famous face spelling paradigm at the parietal area from 340 to 480 ms (P300), from 480 to 600 ms (P600f), and at the fronto-central area from 700 to 800 ms. Offline and online classification results showed that the self-face spelling paradigm accuracies were significantly higher than those of the famous face spelling paradigm at superposing first two times (P < 0.05). Similar results were found for information transfer rates (P < 0.05).

Conclusions: The self-face spelling paradigm significantly improved the performance of the P300-speller system. This has significant practical applications for brain-computer interfaces (BCIs) and could avoid infringement issues caused by using images of other people's faces.