IEEE Transactions on Pattern Analysis and Machine Intelligence

A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

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Brain Computer Interfaces, a Review

Opportunities and methodological challenges in EEG and MEG resting state functional brain network research

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an aging population. This, in turn, places an ever-increasing burden on healthcare due to the increasing prevalence of patients with chronic illnesses and the reducing income-generating population base needed to sustain them. The need to urgently address this healthcare “time bomb” has accelerated the growth in ubiquitous, pervasive, distributed healthcare technologies. The current move from hospital-centric healthcare toward in-home health assessment is aimed at alleviating the burden on healthcare professionals, the health care system and caregivers. This shift will also further increase the comfort for the patient. Advances in signal acquisition, data storage and communication provide for the collection of reliable and useful in-home physiological data. Artifacts, arising from environmental, experimental and physiological factors, degrade signal quality and render the affected part of the signal useless. The magnitude and frequency of these artifacts significantly increases when data collection is moved from the clinic into the home. Signal processing advances have brought about significant improvement in artifact removal over the past few years. This paper reviews the physiological signals most likely to be recorded in the home, documenting the artifacts which occur most frequently and which have the largest degrading effect. A detailed analysis of current artifact removal techniques will then be presented. An evaluation of the advantages and disadvantages of each of the proposed artifact detection and removal techniques, with particular application to the personal healthcare domain, is provided.

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Artifact Removal in Physiological Signals—Practices and Possibilities

A growing number of affective computing researches recently developed a computer system that can recognize an emotional state of the human user to establish affective human-computer interactions. Various measures have been used to estimate emotional states, including self-report, startle response, behavioral response, autonomic measurement, and neurophysiologic measurement. Among them, inferring emotional states from electroencephalography (EEG) has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity. Yet, EEG-based emotional state estimation requires well-designed computational methods to extract information from complex and noisy multichannel EEG data. In this paper, we review the computational methods that have been developed to deduct EEG indices of emotion, to extract emotion-related features, or to classify EEG signals into one of many emotional states. We also propose using sequential Bayesian inference to estimate the continuous emotional state in real time. We present current challenges for building an EEG-based emotion recognition system and suggest some future directions.

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A Review on the Computational Methods for Emotional State Estimation from the Human EEG

The electroencephalogram (EEG) is often contaminated by electromyography (EMG). In this paper, a novel and robust technique is presented to eliminate EMG artifacts from EEG signals in real-time. First, the canonical correlation analysis (CCA) method is applied on the simulated EEG data contaminated by EMG and electrooculography (EOG) artifacts for separating EMG artifacts from EEG signals. The components responsible for EMG artifacts are distinguished from those responsible for brain activity based on the relative low autocorrelation. We demonstrate that the CCA method is more suitable to reconstruct the EMG-free EEG data than independent component analysis (ICA) methods. In addition, by applying CCA to analyze a number of EEG data contaminated by EMG artifacts, a correlation threshold is determined using an unbiased procedure. Hence, CCA can be used to remove EMG artifacts automatically. Finally, an example is given to verify that, after EMG artifacts were removed successfully from the EEG data contaminated by EMG and EOG simultaneously, not only the underlying brain activity signals but the EOG artifacts are preserved with little distortion.

Online removal of muscle artifact from electroencephalogram signals based on canonical correlation analysis.

Frequent occurrence of electrooculography (EOG) artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In this paper, a robust method is presented to automatically eliminate eye-movement and eye-blink artifacts from EEG signals. Independent Component Analysis (ICA) is used to decompose EEG signals into independent components. Moreover, the features of topographies and power spectral densities of those components are extracted to identify eye-movement artifact components, and a support vector machine (SVM) classifier is adopted because it has higher performance than several other classifiers. The classification results show that feature-extraction methods are unsuitable for identifying eye-blink artifact components, and then a novel peak detection algorithm of independent component (PDAIC) is proposed to identify eye-blink artifact components. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove EOG artifacts effectively from EEG signals with little distortion of the underlying brain signals.

Automatic Removal of Eye-Movement and Blink Artifacts from EEG Signals

Frequent occurrence of ocular artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG) In the present paper, a novel and robust technique is proposed to eliminate ocular artifacts from EEG signals in real time Independent Component Analysis (ICA) is used to decompose EEG signals The features of topography and power spectral density of those components are extracted Moreover, we introduce manifold learning algorithm, a recently popular dimensionality reduction technique, to reduce the dimensionality of initial features, and then those new features are fed to a classifier to identify ocular artifacts components A k-nearest neighbor classifier is adopted to classify components because classification results show that manifold learning with the nearest neighbor algorithm works best Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal The results indicate that the method proposed could remove ocular artifacts effectively from EEG signals with little distortion of the underlying brain signals and be satisfied the real-time application

Real-time removal of ocular artifacts from EEG based on independent component analysis and manifold learning

In this study a novel technique is proposed to automatically remove eye-blink artifacts from EEG signals. EEG signal is decomposed into independent components by independent component analysis (ICA) method. First, we demonstrate that feature-extraction method is not very suitable for identifying eye-blink artifacts components. Hence a peak detection algorithm of independent component is proposed to identify eye-blink artifacts components. Finally, the proposed artifact removal method is evaluated on some contaminated EEG signals, and the results show that it is able to effectively remove eye-blink artifacts from EEG signals with little distortion of underlying brain signals.

Automatic removal of eye-blink artifacts based on ICA and peak detection algorithm

Electromyography (EMG) artifacts are the main and serious contaminated sources to the electroencephalogram (EEG) signals. In this paper, a fully automated EMG removal technique based on canonical correlation analysis (CCA) method is presented. CCA method was proved more suitable to reconstruct the EMG-free EEG data than independent component analysis (ICA) methods in the study. Specially, a number of contaminated and clean EEG data were analyzed in order to decide a reasonable correlation threshold, by which this method can remove successfully not only the light EMG artifacts but also heavy EMG artifacts from the EEG data in real-time application with the little distortion of not only the underlying ictal activity signal but the EOG artifacts.

Pei Wang

Papers

Online removal of muscle artifact from electroencephalogram signals based on canonical correlation analysis.

Automatic Removal of Eye-Movement and Blink Artifacts from EEG Signals

Real-time removal of ocular artifacts from EEG based on independent component analysis and manifold learning

Automatic removal of eye-blink artifacts based on ICA and peak detection algorithm

Online EMG artifacts removal from EEG based on blind source separation