A problem that impedes the progress in Brain-Computer Interface (BCI) research is the difficulty in reproducing the results of different papers. Comparing different algorithms at present is very difficult. Some improvements have been made by the use of standard datasets to evaluate different algorithms. However, the lack of a comparison framework still exists. In this paper, we construct a new general comparison framework to compare different algorithms on several standard datasets. All these datasets correspond to sensory motor BCIs, and are obtained from 21 subjects during their operation of synchronous BCIs and 8 subjects using self-paced BCIs. Other researchers can use our framework to compare their own algorithms on their own datasets. We have compared the performance of different popular classification algorithms over these 29 subjects and performed statistical tests to validate our results. Our findings suggest that, for a given subject, the choice of the classifier for a BCI system depends on the feature extraction method used in that BCI system. This is in contrary to most of publications in the field that have used Linear Discriminant Analysis (LDA) as the classifier of choice for BCI systems.

A powerful tool for analyzing the characteristics of the signal in the frequency domain as well as in the time domain is wavelet analysis. To analyze the signal, it can be decomposed into some levels successively by wavelet transform. At each level, decomposition yields two types of components: the approximations component which is the low-frequency high-scale portion of the signal, and the details component which is the high-frequency low-scale portion. The resultant approximations component is decomposed repetitively after each level. This is usually referred to as wavelet decomposition. Fig. 1.a shows a signal decomposed into three levels by wavelet decomposition.

A novel approach is presented for using an eye tracker-based reference instead of EOG for methods that require an EOG reference to remove ocular artifacts (OA) from EEG. It uses a high-speed eye tracker and a new online algorithm for extracting the time course of a blink from eye tracker images to remove both eye movement and blink artifacts. It eliminates the need for EOG electrodes attached to the face, which is critical for practical daily applications. The ability of two adaptive filters (RLS and H^ ) to remove OA is measured using: 1) EOG; 2) frontal EEG only (fEEG); and 3) the eye tracker with frontal EEG (ET + fEEG) as reference inputs. The results are compared for different eye movements and blinks of varying amplitudes at electrodes across the scalp. Both the RLS and H^ methods were shown to benefit from using the proposed eye tracker-based reference (ET + fEEG) instead of either an EOG reference or a reference based on frontal EEG alone.

BACKGROUND:A novel artefact removal algorithm is proposed for a self-paced hybrid brain-computer interface (BCI) system. This hybrid system combines a self-paced BCI with an eye-tracker to operate a virtual keyboard. To select a letter, the user must gaze at the target for at least a specific period of time (dwell time) and then activate the BCI by performing a mental task. Unfortunately, electroencephalogram (EEG) signals are often contaminated with artefacts. Artefacts change the quality of EEG signals and subsequently degrade the BCI's performance.METHODS:To remove artefacts in EEG signals, the proposed algorithm uses the stationary wavelet transform combined with a new adaptive thresholding mechanism. To evaluate the performance of the proposed algorithm and other artefact handling/removal methods, semi-simulated EEG signals (i.e., real EEG signals mixed with simulated artefacts) and real EEG signals obtained from seven participants are used. For real EEG signals, the hybrid BCI system's performance is evaluated in an online-like manner, i.e., using the continuous data from the last session as in a real-time environment.RESULTS:With semi-simulated EEG signals, we show that the proposed algorithm achieves lower signal distortion in both time and frequency domains. With real EEG signals, we demonstrate that for dwell time of 0.0s, the number of false-positives/minute is 2 and the true positive rate (TPR) achieved by the proposed algorithm is 44.7%, which is more than 15.0% higher compared to other state-of-the-art artefact handling methods. As dwell time increases to 1.0s, the TPR increases to 73.1%.CONCLUSIONS:The proposed artefact removal algorithm greatly improves the BCI's performance. It also has the following advantages: a) it does not require additional electrooculogram/electromyogram channels, long data segments or a large number of EEG channels, b) it allows real-time processing, and c) it reduces signal distortion.

As the characteristics of EEG signals change over time, updating the classifier of a brain computer interface, BCI, (over time) would improve the performance of the system. Developing an adaptive classifier for a self-paced BCI however is not easy because the user's intention (and therefore the true labels of the EEG signals) are not known during the operation of the system. For certain applications, it may be possible to predict the labels of some of the EEG segments using some information about the user's state (e.g., the error potentials or gaze information). This study proposes a method that adaptively updates the classifier of a self-paced BCI in a supervised or semi-supervised manner, using those EEG segments whose labels can be predicted. We employ the eye position information obtained from an eye-tracker to predict the EEG labels. This eye-tracker is also used along with a self-paced BCI to form a hybrid BCI system. The results obtained from seven individuals show that the proposed algorithm outperforms the non-adaptive and other unsupervised adaptive classifiers. It achieves a true positive rate of 49.7% and lowers the number of false positives significantly to only 2.2 FPs/minute.

A recently collected EEG dataset is analyzed and processed in order to evaluate the performance of a previously designed brain-computer interface (BCI) system. The EEG signals are collected from 29 channels distributed over the scalp. Four subjects completed three sessions each by performing four different mental tasks during each session. The BCI is designed in such a way that only one of the mental tasks can activate it. One important advantage of this BCI is its simplicity, since autoregressive modeling and quadratic discriminant analysis are used for feature extraction and classification, respectively. The autoregressive order which yields the best overall performance is obtained during a fivefold nested cross-validation process. The results are promising as the false positive rates are zero while the true positive rates are sufficiently high (67.26% average).

A hybrid brain–computer interface (BCI) system that combines a self-paced BCI and an eye-tracker is proposed for text-entry applications. To make a text-entry of a letter/word, the user must gaze at the target for at least a specific period of time (called the dwell time) and then activate the self-paced BCI with an attempted hand extension. Although the self-paced BCI is available for use at any time, a built-in sleep mode is activated when the user is not looking at a letter/word or when the user gazes at a letter/word for less than the dwell time. Such a design has the advantage of greatly minimizing the false positive outcomes compared to the state-of-art self-paced BCIs. To further improve the system's performance, a method that adaptively updates the BCI classifier is also proposed. The results from seven able-bodied individuals show great improvements compared to the pure self-paced BCI. For dwell times of 0.75 and 1.00 s, the number of false-positives/minute is significantly reduced to 2.5 and 1.7, at acceptable average true positive rates of 54.5% and 54.1%, respectively.

An algorithm that detects various types of artefacts in a self-paced brain-computer interface is proposed. This method achieves similar performance to the state-of-art methods but has the following advantages, 1) being fully automatic (the threshold values are automatically found), 2) does not use additional EOG, EMG or frontal/temporal EEG channels, and 3) computationally inexpensive. The data were collected from the motor cortex areas using 15 EEG signals. The features extracted include the maximum amplitude of EEG signals and the stationary wavelet transform coefficients. To detect the artefacts, a simple threshold-based classifier is applied. The experimental results demonstrate that when detecting ocular and electrode movement artefacts, the method has a sensitivity (correctly detecting segments with artefacts) of 77.7%, and specificity (correctly detecting artefact-free segments) of 82.8%. For artefacts with more pronounced effects in the high frequency bands (e. g. facial muscle artefacts), the sensitivity and specificity are 83.5% and 70.3% respectively.

In this paper, we address the problem of finding the best wavelet basis in wavelet packet analysis for applications based on classification. We implement and evaluate our proposed method in the design of a self-paced 2-state mental task-based brain-computer interface (BCI) as one possible type of classification-based applications. The autoregressive coefficients of the best wavelet basis are concatenated to form the feature vector. The 2-stage classification process is based on quadratic discriminant analysis and majority voting. Seventeen wavelets from 2 different families are tested. A 5×5 cross-validation process is per-formed twice to do model selection and system performance evaluation. The results show that the proposed method can be well applied to BCI systems.

Information and communication technologies (ICT) are directly or indirectly a form of assistive technology. Indeed, many forms of new emerging ICT have the potential to provide opportunities for persons with disabilities to obtain inclusion in the mainstream of society to an extent never before accomplished. However, due to the rapid and ubiquitous insertion of these technologies into our society and given that most of these technologies are not accessible to persons with disabilities they are forming new barriers rather than opening up new opportunities. New strategies need to be adopted to deal with this trend and, particularly in the relatively near term, it is argued that the most important strategy is the development of enforceable regulations and standards that impact the accessibility of these new emerging technologies.

INTRODUCTION Sexual health is often severely impacted after spinal cord injury (SCI). Current research has primarily addressed male erection and fertility, when in fact pleasure and orgasm are top priorities for functional recovery. Sensory substitution technology operates by communicating input from a lost sensory pathway to another intact sensory modality. It was hypothesized that through training and neuroplasticity, mapped tongue sensations would be interpreted as sensory perceptions arising from insensate genitalia, and improve the sexual experience. AIM To report the development of a sensory substitution system for the sexual rehabilitation of men with chronic SCI. METHODS Subjects performed sexual self-stimulation while using a novel sensory substitution device that mapped the stroking motion of the hand to a congruous flow of electrocutaneous sensations on the tongue. MAIN OUTCOME MEASURES Three questionnaires, along with structured interviews, were used to rate the perceived sexual sensations following each training session. RESULTS Subjects completed 20 sessions over approximately 8 weeks of training. Each subject reported an increased level of sexual pleasure soon after training with the device. Each subject also reported specific perceptions of cutaneous-like sensations below their lesion that matched their hand motion. Later sessions, while remaining pleasurable and interesting, were inconsistent, and no subject reported an orgasmic feeling during a session. The subjects were all interested in continuing training with the device at home, if possible, in the future. CONCLUSIONS This study is the first to show that sensory substitution is a possible therapeutic avenue for sexual rehabilitation in people lacking normal genital sexual sensations. However more research, for instance on frequency and duration of training, is needed in order to induce functional lasting neuroplasticity. In the near term, SCI rehabilitation should more fully address sexuality and the role of neuroplasticity for promoting the maximal potential for sexual pleasure and orgasm.

To design a practical brain-computer interface, the high rate of false activation and the high number of necessary electrodes are two major problems that must be addressed. The objective of this study is to design a brain interface system that requires very few channels, has a zero false activation rate and a high true activation rate. To attain this objective, a brain-computer interface that is EEG-based and that is activated by mental tasks is proposed. The system is custom designed for each subject. For each subject, the most discriminatory mental task that yields a zero false activation rate is determined. By keeping the false positive rate at zero, the number of channels needed is reduced. We show that we can obtain a false positive rate of zero value and a true positive rate in the range of 71.96% to 77.61% with only three electrode channels. The dataset used was not collected in a self-paced paradigm; however, it is employed to show that the design of a self-paced interface is feasible. EEG signals of four subjects performing five mental tasks are used as data. Applying fast and simple approaches like the autoregressive modeling and the quadratic discriminant analysis as the feature extraction and classification methods, respectively, is another advantage of the present work.

This study employs compressive sensing (CS) for accelerating the overall data processing in a brain-computer interface (BCI). CS is a joint signal acquisition and compression scheme. By projecting EEG data streams onto a lower dimensional basis that is incoherent with their inherent structure, CS compresses the data and preserves their salient information. This paper presents a novel self-paced BCI (SBCI) that extracts compact and relevant features from EEG using CS. To detect an intentional control (IC) state from user's EEG, the signal structure of a specific voluntary movement that is common in all trials is identified. This compressed subset of basis functions is constructed during the BCIs training mode and is then used to acquire features during its testing mode. Experimental results show that our proposed method can efficiently detect voluntary movements for a SBCI, with potential speed increases up to 12 times from the state-of-art prototype.

Most brain-computer interface applications in real-life suffer from the high rate of false activations. The ultimate goal when designing brain-computer interfaces is to reach the zero false activation rate while the true activation rate is kept at a high level. In this study, a brain-computer interface design is shown to have a zero false activation rate. The interface is based on different mental tasks. It is custom designed to every subject and to every mental task. The most discriminatory mental task for each subject is determined. We use the autoregressive modeling as the feature extraction method. The classification is performed by a radial basis function neural network. The EEG signals of four subjects during five mental tasks are used. The order of autoregressive model is varied from 2 to 20 and custom designed for each mental task and each subject in the cross-validation stage. The performance of the brain-computer interfaces based on the most discriminatory mental tasks is shown to be highly promising since the false positive rate reaches zero while the mean of the true positive rate obtained is above 70%.

The feasibility of having a self-paced brain–computer interface (BCI) based on mental tasks is investigated. The EEG signals of four subjects performing five mental tasks each are used in the design of a 2-state self-paced BCI. The output of the BCI should only be activated when the subject performs a specific mental task and should remain inactive otherwise. For each subject and each task, the feature coefficient and the classifier that yield the best performance are selected, using the autoregressive coefficients as the features. The classifier with a zero false positive rate and the highest true positive rate is selected as the best classifier. The classifiers tested include: linear discriminant analysis, quadratic discriminant analysis, Mahalanobis discriminant analysis, support vector machine, and radial basis function neural network. The results show that: (1) some classifiers obtained the desired zero false positive rate; (2) the linear discriminant analysis classifier does not yield acceptable performance; (3) the quadratic discriminant analysis classifier outperforms the Mahalanobis discriminant analysis classifier and performs almost as well as the radial basis function neural network; and (4) the support vector machine classifier has the highest true positive rates but unfortunately has nonzero false positive rates in most cases.

The effects of two factors on the performance of online ocular artifact (OA) removal methods on real electroencephalogram (EEG) data are evaluated. A new metric is proposed that captures both the amount of artifact removed and the likelihood that a given method would distort the underlying EEG. The metric is then used to measure the performance of an existing on-line OA removal algorithm during periods of (i) eye movements and blinks (EM), (ii) a motor related potential (MRP), and (iii) neither EM nor MRP. The results show that the performance of the algorithm is significantly (p < .05) affected by both EM and MRP. Also, the algorithm - like many others requires a reference electro-oculogram (EOG) signal, yet for certain applications (e.g., a brain computer interface or 130) it is preferable or necessary to avoid attaching electrodes around the eyes. Thus, the new metric is also used to investigate the effects of using 3 frontal EEG channels as the reference signal instead of EOG. The results show that replacing the EOG with the selected 3 EEG channels does not significantly (p < .05) affect the performance of the algorithm, making it a viable option for online applications where the use of EOG is not suitable (e.g., BCI).

At present, brain-computer interfaces cannot be used in real-life applications mainly because of their high false activation rates. To achieve a zero false positive rate, a mental task-based brain-computer interface custom designed for each subject and each task is proposed. The most discriminatory mental task is determined for each subject. We used the EEG signals of four subjects recorded while they were performing five different mental tasks. Autoregressive modeling and stationary wavelet transform are used in the process of feature extraction. Classification is based on quadratic discriminant analysis. For the most discriminatory mental task of each subject, we achieved a false positive rate of zero value while the true positive rate obtained was above 60%.

The stationary wavelet packet analysis is exploited for the first time in the design of a self-paced BCI based on mental tasks. The BCI system is custom designed to achieve a zero false positive rate, as false activations highly restricts the applications of BCIs in real life. The EEG signals of four subjects performing five different mental tasks are used as the dataset. The stationary wavelet packets decompose the signal into eight components. The features used are the autoregressive coefficients obtained by applying autoregressive modeling on the resultant wavelet components. Classification is a two-stage process. The first stage is based on quadratic discriminant analysis which is extremely fast. The second stage is a simple majority voting classifier. During model selection, which is performed via 5-folded cross-validation, the combination of decomposed components and the autoregressive model order that yield the best performance are selected. Results show enhancements in the overall performance for three subjects comparing to our previously designed BCI.