EEG-based Based Brain-computer Interfaces Research Articles

A New Self-Regulated Neuro-Fuzzy Framework for Classification of EEG Signals in Motor Imagery BCI

One of the major problems associated with the motor imagery (MI) electroencephalogram (EEG) based brain–computer interface (BCI) classifications is the informative ambiguities mainly caused by interferences of artifacts and nonstationarities in EEG signals. Other factors containing mislabeling or misleading MI EEG trials might also cause more uncertainties in training datasets that lead to decline in classification performance. This paper proposes a new framework to achieve more efficient classification in multiclass MI EEG-based BCIs. An artifact rejected common spatial pattern (AR-CSP) method is proposed for feature extraction in order to cope with the interferences of artifacts. A self-regulated adaptive resonance theory based neuro-fuzzy classifier that is referred to as “self-regulated supervised Gaussian fuzzy adaptive system Art (SRSG-FasArt)” is introduced to deal with EEG nonstationarities. A metacognitive self-regulatory-based learning algorithm is also proposed to more efficiently deal with the uncertainties. The algorithm captures the training data samples by priority and automatically creates, upgrades, or prunes the fuzzy rules by scanning the knowledge content existing in the data patterns and the created rules. The mechanism improves the generalization capability of the SRSG-FasArt and prevents over-training. The performance of the proposed cooperative framework of AR-CSP and SRSG-FasArt is evaluated using the BCI competition IV dataset 2a. The results indicate more accurate and efficient BCI classification compared to the existing frameworks.

EEG-based BCI and video games: a progress report

This paper presents a systematic review of electroencephalography (EEG)-based brain---computer interfaces (BCIs) used in the video games, a vibrant field of research that touches upon all relevant questions concerning the future directions of BCI. The paper examines the progress of BCI research with regard to games and shows that gaming applications offer numerous advantages by orienting BCI to concerns and expectations of a gaming application. Different BCI paradigms are investigated, and future directions are discussed.

Abstract TP144: EEG-Based Brain Computer Interface Therapy for the Restoration of Distal Upper Extremity Motor Function

Introduction: This current study is part of a larger, on-going clinical trial (NCT02098265), which evaluates non-invasive electroencephalographic (EEG) based brain-computer interface(BCI) therapy for restoration of distal upper extremity motor function in stroke survivors. We acquired data for 8 participants (mean age= 64 years) presenting with varying levels of upper-extremity motor deficits and chronicity since stroke. EEG based BCI task-related performance outcomes were compared with distal extremity behavioral testing on the 9-Hole Peg Test - 9HPT) to illustrate the relationship of BCI training performance on behavioral performance. Methods: EEG data is acquired with BCI2000, a 16 channel recording system (g.LADYbird-g. GAMMAsys-g. USBamp, Guger Technologies, Graz, Austria) with electrodes positioned according to the standard 10-20 system over the sensorimotor cortex at C3 & C4. Participants performed a hand movement task; randomly cued to move either their affected or unaffected hand depending on the appearance of virtual target and cursor on the screen. 9HPT Data was selected from two BCI conditions: 1) BCI with visual stimulus only, and 2) BCI with visual stimulation plus functional electrical stimulation (FES) of the impaired arm, and tongue stimulation (TS) at four time-points: baseline (prior to BCI therapy), mid-point of BCI therapy, post-therapy, and one month post-therapy. Results: BCI visual plus stimulus for the unaffected side was found to best relate to 9HPT scores (p= 0.0928) with a trend to significance. BCI visual plus stimulus for the affected side was not shown to correlate with 9HPT scores (p= 0.2655). The results suggest that 9HPT scores better relate with average task accuracy when the TS and FES adjuvants are incorporated (BCI stimulus vs 9H PT unaffected p= 0.09284, BCI stimulus vs 9H PT affected p= 0.26553) compared to the BCI visual only task (BCI visual vs 9HPT unaffected p=0.2702, BCI visual vs 9HPT affected (p= 0.89276). Conclusion: Non-invasive EEG-based BCI therapy may be suitable for the restoration of distal upper extremity motor function but average task accuracy does not appear to be a valid indicator of motor improvement. This finding suggests that visual only BCI systems are inadequate for motor rehabilitation.

Enheduanna – A Manifesto of Falling: first demonstration of a live brain-computer cinema performance with multi-brain BCI interaction for one performer and two audience members

ABSTRACTThe new commercial-grade Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) have led to a phenomenal development of applications across health, entertainment and the arts, while an increasing interest in multi-brain interaction has emerged. In the arts, there is already a number of works that involve the interaction of more than one participants with the use of EEG-based BCIs. However, the field of live brain-computer cinema and mixed-media performances is rather new, compared to installations and music performances that involve multi-brain BCIs. In this context, we present the particular challenges involved. We discuss Enheduanna – A Manifesto of Falling, the first demonstration of a live brain-computer cinema performance that enables the real-time brain-activity interaction of one performer and two audience members; and we take a cognitive perspective on the implementation of a new passive multi-brain EEG-based BCI system to realise our creative concept. This article also presents the preliminary results and future work.

EEG-based BCI for the linear control of an upper-limb neuroprosthesis

Assistive technologies help patients to reacquire interacting capabilities with the environment and improve their quality of life. In this manuscript we present a feasibility study in which healthy users were able to use a non-invasive Motor Imagery (MI)-based brain computer interface (BCI) to achieve linear control of an upper-limb functional electrical stimulation (FES) controlled neuro-prosthesis. The linear control allowed the real-time computation of a continuous control signal that was used by the FES system to physically set the stimulation parameters to control the upper-limb position. Even if the nature of the task makes the operation very challenging, the participants achieved a mean selection accuracy of 82.5% in a target selection experiment. An analysis of limb kinematics as well as the positioning precision was performed, showing the viability of using a BCI–FES system to control upper-limb reaching movements. The results of this study constitute an accurate use of an online non-invasive BCI to operate a FES-neuroprosthesis setting a step toward the recovery of the control of an impaired limb with the sole use of brain activity.

A real-time classification algorithm for EEG-based BCI driven by self-induced emotions

Background and objectiveThe aim of this paper is to provide an efficient, parametric, general, and completely automatic real time classification method of electroencephalography (EEG) signals obtained from self-induced emotions. The particular characteristics of the considered low-amplitude signals (a self-induced emotion produces a signal whose amplitude is about 15% of a really experienced emotion) require exploring and adapting strategies like the Wavelet Transform, the Principal Component Analysis (PCA) and the Support Vector Machine (SVM) for signal processing, analysis and classification. Moreover, the method is thought to be used in a multi-emotions based Brain Computer Interface (BCI) and, for this reason, an ad hoc shrewdness is assumed. MethodThe peculiarity of the brain activation requires ad-hoc signal processing by wavelet decomposition, and the definition of a set of features for signal characterization in order to discriminate different self-induced emotions. The proposed method is a two stages algorithm, completely parameterized, aiming at a multi-class classification and may be considered in the framework of machine learning. The first stage, the calibration, is off-line and is devoted at the signal processing, the determination of the features and at the training of a classifier. The second stage, the real-time one, is the test on new data. The PCA theory is applied to avoid redundancy in the set of features whereas the classification of the selected features, and therefore of the signals, is obtained by the SVM. ResultsSome experimental tests have been conducted on EEG signals proposing a binary BCI, based on the self-induced disgust produced by remembering an unpleasant odor. Since in literature it has been shown that this emotion mainly involves the right hemisphere and in particular the T8 channel, the classification procedure is tested by using just T8, though the average accuracy is calculated and reported also for the whole set of the measured channels. ConclusionsThe obtained classification results are encouraging with percentage of success that is, in the average for the whole set of the examined subjects, above 90%. An ongoing work is the application of the proposed procedure to map a large set of emotions with EEG and to establish the EEG headset with the minimal number of channels to allow the recognition of a significant range of emotions both in the field of affective computing and in the development of auxiliary communication tools for subjects affected by severe disabilities.

Analyze EEG signals with extreme learning machine based on PMIS feature selection

An electroencephalogram (EEG)-based brain–computer interface (BCI) system can be used to determine the intents for a paralyzed user by analyzing the EEG signals recorded from his scalp. The key technology of BCI systems is feature extraction and pattern recognition algorithm. Many of the BCI research show that nonlinear classification algorithms perform better than the linear, but they are usually far slower than required. Besides, many of these methods employ less significant features and could make the classification process less efficient. In this paper, a method is provided to identify different patterns of EEG signals. A fast nonlinear classification algorithm extreme learning machine (ELM) is provided to identify two different EEG signals. After feature extraction, we adopt a nonlinear feature selection algorithm partial mutual information-based feature selection (PMIS) to eliminate the less significant features. The experimental result shows that (1) ELM can discriminate the two patterns of EEG signals with a relatively satisfactory accuracy and (2) PMIS feature selection can find out the important features effectively and improve the classification accuracy. The final accuracy is 91.5 % against the best existed result of 88.7 % for the tested data set. The classification performance may be further improved by adding complementary features from EEG. This approach may eventually lead to a reliable EEG-based BCI studies.

Improving brain–computer interface classification using adaptive common spatial patterns

Common Spatial Patterns (CSP) is a widely used spatial filtering technique for electroencephalography (EEG)-based brain–computer interface (BCI). It is a two-class supervised technique that needs subject-specific training data. Due to EEG nonstationarity, EEG signal may exhibit significant intra- and inter-subject variation. As a result, spatial filters learned from a subject may not perform well for data acquired from the same subject at a different time or from other subjects performing the same task. Studies have been performed to improve CSP׳s performance by adding regularization terms into the training. Most of them require target subjects׳ training data with known class labels. In this work, an adaptive CSP (ACSP) method is proposed to analyze single trial EEG data from single and multiple subjects. The method does not estimate target data׳s class labels during the adaptive learning and updates spatial filters for both classes simultaneously. The proposed method was evaluated based on a comparison study with the classic CSP and several CSP-based adaptive methods using motor imagery EEG data from BCI competitions. Experimental results indicate that the proposed method can improve the classification performance as compared to the other methods. For circumstances where true class labels of target data are not instantly available, it was examined if adding classified target data to training data would improve the ACSP learning. Experimental results show that it would be better to exclude them from the training data. The proposed ACSP method can be performed in real-time and is potentially applicable to various EEG-based BCI applications.

EEG-based Brain-Computer Interfaces: An Overview of Basic Concepts and Clinical Applications in Neurorehabilitation

Some patients are no longer able to communicate effectively or even interact with the outside world in ways that most of us take for granted. In the most severe cases, tetraplegic or post-stroke patients are literally 'locked in' their bodies, unable to exert any motor control after, for example, a spinal cord injury or a brainstem stroke, requiring alternative methods of communication and control. But we suggest that, in the near future, their brains may offer them a way out. Non-invasive electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be characterized by the technique used to measure brain activity and by the way that different brain signals are translated into commands that control an effector (e.g., controlling a computer cursor for word processing and accessing the internet). This review focuses on the basic concepts of EEG-based BCI, the main advances in communication, motor control restoration and the downregulation of cortical activity, and the mirror neuron system (MNS) in the context of BCI. The latter appears to be relevant for clinical applications in the coming years, particularly for severely limited patients. Hypothetically, MNS could provide a robust way to map neural activity to behavior, representing the high-level information about goals and intentions of these patients. Non-invasive EEG-based BCIs allow brain-derived communication in patients with amyotrophic lateral sclerosis and motor control restoration in patients after spinal cord injury and stroke. Epilepsy and attention deficit and hyperactive disorder patients were able to downregulate their cortical activity. Given the rapid progression of EEG-based BCI research over the last few years and the swift ascent of computer processing speeds and signal analysis techniques, we suggest that emerging ideas (e.g., MNS in the context of BCI) related to clinical neurorehabilitation of severely limited patients will generate viable clinical applications in the near future.

Electro-encephalogram based brain–computer interface: improved performance by mental practice and concentration skills

Mental imagination is the essential part of the most EEG-based communication systems. Thus, the quality of mental rehearsal, the degree of imagined effort, and mind controllability should have a major effect on the performance of electro-encephalogram (EEG) based brain-computer interface (BCI). It is now well established that mental practice using motor imagery improves motor skills. The effects of mental practice on motor skill learning are the result of practice on central motor programming. According to this view, it seems logical that mental practice should modify the neuronal activity in the primary sensorimotor areas and consequently change the performance of EEG-based BCI. For developing a practical BCI system, recognizing the resting state with eyes opened and the imagined voluntary movement is important. For this purpose, the mind should be able to focus on a single goal for a period of time, without deviation to another context. In this work, we are going to examine the role of mental practice and concentration skills on the EEG control during imaginative hand movements. The results show that the mental practice and concentration can generally improve the classification accuracy of the EEG patterns. It is found that mental training has a significant effect on the classification accuracy over the primary motor cortex and frontal area.