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Herman, Pawel Andrzej, 1979-, et al.
(author)
Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification
2008
In: IEEE transactions on neural systems and rehabilitation engineering. - 1534-4320 .- 1558-0210. ; 16:4, s. 317-326
Journal article (peer-reviewed) abstract
The quantification of the spectral content of electroencephalogram (EEG) recordings has a substantial role in clinical and scientific applications. It is of particular relevance in the analysis of event-related brain oscillatory responses. This work is focused on the identification and quantification of relevant frequency patterns in motor imagery (MI) related EEGs utilized for brain--computer interface (BCI) purposes. The main objective of the paper is to perform comparative analysis of different approaches to spectral signal representation such as power spectral density (PSD) techniques, atomic decompositions, time-frequency (t-f) energy distributions, continuous and discrete wavelet approaches, from which band power features can be extracted and used in the framework of MI classification. The emphasis is on identifying discriminative properties of the feature sets representing EEG trials recorded during imagination of either left-- or right-hand movement. Feature separability is quantified in the offline study using the classification accuracy (CA) rate obtained with linear and nonlinear classifiers. PSD approaches demonstrate the most consistent robustness and effectiveness in extracting the distinctive spectral patterns for accurately discriminating between left and right MI induced EEGs. This observation is based on an analysis of data recorded from eleven subjects over two sessions of BCI experiments. In addition, generalization capabilities of the classifiers reflected in their intersession performance are discussed in the paper..
4.
Herman, Pawel Andrzej, 1979-, et al.
(author)
Design and on-line evaluation of type-2 fuzzy logic system-based framework for handling uncertainties in BCI classification
2008
In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. - 9781424418152 ; , s. 4242-4245
Conference paper (peer-reviewed) abstract
The practical applicability of brain-computer interface (BCI) technology is limited due to its insufficient reliability and robustness. One of the major problems in this regard is the extensive variability and inconsistency of brain signal patterns, observed especially in electroencephalogram (EEG). This paper presents a fuzzy logic (FL) approach to the problem of handling of the resultant uncertainty effects. In particular, it outlines the design of a novel type-2 FL system (T2FLS) classifier within the framework of an EEG-based BCI, and examines its on-line applicability in the presence of shortand long-term nonstationarities of spectral EEG correlates of motor imagery (imagination of left vs. right hand movement). The developed system is shown to effectively cope with realtime constraints. In addition, a comparative post hoc analysis has revealed that the proposed T2FLS classifier outperforms conventional BCI methods, like LDA and SVM, in terms of the maximum classification accuracy (CA) rates by a relatively small, yet statistically significant, margin.
5.
Herman, Pawel Andrzej, 1979-, et al.
(author)
Designing a robust type-2 fuzzy logic classifier for non-stationary systems with application in brain-computer interfacing
2008
In: IEEE SMC 2008. ; , s. 1343-1349
Conference paper (peer-reviewed) abstract
Type-2 (T2) fuzzy logic (FL) systems (T2FLSs) have shown a remarkable potential in dealing with uncertain data resulting from real-world systems with non-stationary characteristics. This paper reports on novel developments in interval T2FLS (IT2FLS) classifier design methodology so that system non-stationarities can be effectively handled. In general, the approach presented here rests on a general concept of twostage FLS design in which an initial rule base structure is first initialized and then system parameters are globally optimized. The proposed incremental enhancements of existing fuzzy techniques, adopted from the area of conventional type-1 (T1) FL, are heuristic in nature. The IT2FLS design methods have been empirically verified in this work in the realm of pattern recognition. In particular, the potential and the suitability of IT2FLS to the problem of classification of motor imagery (MI) related patterns in electroencephalogram (EEG) recordings has been investigated. The outcome of this study bears direct relevance to the development of EEG-based brain-computer interfaces (BCIs) since the problem under examination poses a major difficulty for the state-of-the-art BCI methods. The IT2FLS classifier is evaluated in this work on multi-session EEG data sets in the framework of an off-line BCI. Its performance is quantified in terms of the classification accuracy (CA) rates and has been found to be favorable to that of analogous systems employing a conventional T1 FLS, along with linear discriminant analysis (LDA) and support vector machine (SVM), commonly utilized in MI-based BCI systems.
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Herman, Pawel Andrzej, 1979-, et al.
(author)
Investigation of the Type-2 Fuzzy Logic Approach to Classification in an EEG-based Brain-Computer Interface
2005
In: PROCEEDINGS OF ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ; , s. 5354-5357
Conference paper (peer-reviewed) abstract
Analysis of electroencephalogram (EEG) requires a framework that facilitates handling the uncertainties associated with the varying brain dynamics and the presence of noise. Recently, the type-2 fuzzy logic systems (T2 FLSs) have been found effective in modeling uncertain data. This paper examines the potential of the T2 FLS methodology in devising an EEG-based brain-computer interface (BCI). In particular, a T2 FLS has been designed to classify imaginary left and right hand movements based on time-frequency information extracted from the EEG with the short time Fourier transform (STFT). Robustness of the method has also been verified in the presence of additive noise. The performance of the classifier is quantified with the classification accuracy (CA). The T2 fuzzy classifier has been proven to outperform its type-1 (T1) counterpart on all data sets recorded from three subjects examined. It has also compared favorably to the well known classifier based on linear discriminant analysis (LDA).
8.
Prasad, G., et al.
(author)
Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery : A feasibility study
2010
In: Journal of NeuroEngineering and Rehabilitation. - : Springer Science and Business Media LLC. - 1743-0003. ; 7:1, s. 60-
Journal article (peer-reviewed) abstract
There is now sufficient evidence that using a rehabilitation protocol involving motor imagery (MI) practice in conjunction with physical practice (PP) of goal-directed rehabilitation tasks leads to enhanced functional recovery of paralyzed limbs among stroke sufferers. It is however difficult to confirm patient engagement during an MI in the absence of any on-line measure. Fortunately an EEG-based brain-computer interface (BCI) can provide an on-line measure of MI activity as a neurofeedback for the BCI user to help him/her focus better on the MI task. However initial performance of novice BCI users may be quite moderate and may cause frustration. This paper reports a pilot study in which a BCI system is used to provide a computer game-based neurofeedback to stroke participants during the MI part of a protocol. Methods. The participants included five chronic hemiplegic stroke sufferers. Participants received up to twelve 30-minute MI practice sessions (in conjunction with PP sessions of the same duration) on 2 days a week for 6 weeks. The BCI neurofeedback performance was evaluated based on the MI task classification accuracy (CA) rate. A set of outcome measures including action research arm test (ARAT) and grip strength (GS), was made use of in assessing the upper limb functional recovery. In addition, since stroke sufferers often experience physical tiredness, which may influence the protocol effectiveness, their fatigue and mood levels were assessed regularly. Results. Positive improvement in at least one of the outcome measures was observed in all the participants, while improvements approached a minimal clinically important difference (MCID) for the ARAT. The on-line CA of MI induced sensorimotor rhythm (SMR) modulation patterns in the form of lateralized event-related desynchronization (ERD) and event-related synchronization (ERS) effects, for novice participants was in a moderate range of 60-75% within the limited 12 training sessions. The ERD/ERS change from the first to the last session was statistically significant for only two participants. Conclusions. Overall the crucial observation is that the moderate BCI classification performance did not impede the positive rehabilitation trends as quantified with the rehabilitation outcome measures adopted in this study. Therefore it can be concluded that the BCI supported MI is a feasible intervention as part of a post-stroke rehabilitation protocol combining both PP and MI practice of rehabilitation tasks. Although these findings are promising, the scope of the final conclusions is limited by the small sample size and the lack of a control group.
9.
Prasad, G., et al.
(author)
Using motor imagery based brain-computer interface for post-stroke rehabilitation
2009
In: 2009 4TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING. - 9781424420735 ; , s. 251-255
Conference paper (peer-reviewed) abstract
There is now sufficient evidence that using a rehabilitation protocol involving motor imagery (MI) practice (or mental practice (MP)) in conjunction with physical practice (PP) of goal-directed rehabilitation tasks leads to enhanced functional recovery of paralyzed limbs among stroke sufferers. It is however difficult to ensure patient engagement during MP in the absence of any on-line measure of the MP. Fortunately in an EEG-based brain-computer interface (BCI), an on-line measure of MI activity is used to devise neurofeedback for the BCI user to help him/her focus better on the task. This paper reports a pilot study in which an EEG-based BCI system is used to provide neurofeedback to stroke participants during the MP part of the rehabilitation protocol. This helps patients to undertake the MP with stronger focus. The participants included five chronic stroke sufferers. The trial was undertaken for 12 sessions over a period of 6 weeks. A set of rehabilitation outcome measures including action research arm test (ARAT) and motricity index was made use of in assessing functional recovery. Moderate improvements approaching a minimal clinically important difference (MCID) were observed for the ARAT. Small positive improvements were also observed in other outcome measures. Participants appeared highly enthusiastic about participating in the study and regularly attended all the sessions. Although without a randomized control trial, it is difficult to ascertain whether the enhanced rehabilitation gain is primarily because of BCI neurofeedack, the positive gains in outcome measures demonstrate the potential and feasibility of using BCI for post-stroke rehabilitation.
