Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals

Dual-layer electroencephalography data during real-world table tennis

Real-world settings are necessary to improve the ecological validity of neuroscience research, and electroencephalography (EEG) facilitates mobile electrocortical recordings because of its easy portability and high temporal resolution. Table tennis is a whole-body, goal-directed sport that requires constant visuomotor feedback, anticipation, strategic decision-making, object interception, and performance monitoring - making it an interesting testbed for a variety of neuroscience studies. Although traditionally plagued by artifact contamination, recent advances in signal processing and hardware approaches, such as the dual-layer approach, have allowed high fidelity EEG recordings during whole-body maneuvers. Here, we present a dual-layer EEG dataset from 25 healthy human participants playing table tennis with a human opponent and a ball machine. Our dataset includes synchronized, multivariate time series recordings from 120 scalp electrodes, 120 noise electrodes, 8 neck electromyography electrodes, and inertial measurement units on the participant, paddles, and ball machine to record hit events. We also include de-identified T1 anatomical MR images and digitized electrode locations to create subject-specific head models for source localization. In addition, we provide anonymized video recordings and Adobe Premiere project files with hit events labeled (originally used to mark successful/missed hits). Researchers could use the videos to mark their own events of interest. We formatted our dataset in the Brain Imaging Data Structure (BIDS) format to facilitate data reuse and to adhere to the scientific community's new organization standard.

ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals

Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML’s popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository.

Characterizing and Removing Artifacts Using Dual-Layer EEG during Table Tennis

Researchers can improve the ecological validity of brain research by studying humans moving in real-world settings. Recent work shows that dual-layer EEG can improve the fidelity of electrocortical recordings during gait, but it is unclear whether these positive results extrapolate to non-locomotor paradigms. For our study, we recorded brain activity with dual-layer EEG while participants played table tennis, a whole-body, responsive sport that could help investigate visuomotor feedback, object interception, and performance monitoring. We characterized artifacts with time-frequency analyses and correlated scalp and reference noise data to determine how well different sensors captured artifacts. As expected, individual scalp channels correlated more with noise-matched channel time series than with head and body acceleration. We then compared artifact removal methods with and without the use of the dual-layer noise electrodes. Independent Component Analysis separated channels into components, and we counted the number of high-quality brain components based on the fit of a dipole model and using an automated labeling algorithm. We found that using noise electrodes for data processing provided cleaner brain components. These results advance technological approaches for recording high fidelity brain dynamics in human behaviors requiring whole body movement, which will be useful for brain science research.

Binary Controller Based on the Electrical Activity Related to Head Yaw Rotation

A human machine interface (HMI) is presented to switch on/off lights according to the head left/right yaw rotation. The HMI consists of a cap, which can acquire the brain’s electrical activity (i.e., an electroencephalogram, EEG) sampled at 500 Hz on 8 channels with electrodes that are positioned according to the standard 10–20 system. In addition, the HMI includes a controller based on an input–output function that can compute the head position (defined as left, right, and forward position with respect to yaw angle) considering short intervals (10 samples) of the signals coming from three electrodes positioned in O1, O2, and Cz. An artificial neural network (ANN) training based on a Levenberg–Marquardt backpropagation algorithm was used to identify the input–output function. The HMI controller was tested on 22 participants. The proposed classifier achieved an average accuracy of 88% with the best value of 96.85%. After calibration for each specific subject, the HMI was used as a binary controller to verify its ability to switch on/off lamps according to head turning movement. The correct prediction of the head movements was greater than 75% in 90% of the participants when performing the test with open eyes. If the subjects carried out the experiments with closed eyes, the prediction accuracy reached 75% of correctness in 11 participants out of 22. One participant controlled the light system in both experiments, open and closed eyes, with 100% success. The control results achieved in this work can be considered as an important milestone towards humanoid neck systems.

Medical Technology: A Systematic Review on Medical Devices Utilized for Epilepsy Prediction and Management

BACKGROUND

Epilepsy is a devastating neurological disorder that affects nearly 70 million people worldwide. Epilepsy causes uncontrollable, unprovoked and unpredictable seizures that reduce the quality of life of those afflicted, with 1-9 epileptic patient deaths per 1000 patients occurring annually due to sudden unexpected death in epilepsy (SUDEP). Predicting the onset of seizures and managing them may help patients from harming themselves and may improve their well-being. For a long time, electroencephalography (EEG) devices have been the mainstay for seizure detection and monitoring. This systematic review aimed to elucidate and critically evaluate the latest advancements in medical devices, besides EEG, that have been proposed for the management and prediction of epileptic seizures. A literature search was performed on three databases, PubMed, Scopus and EMBASE.

METHODS

Following title/abstract screening by two independent reviewers, 27 articles were selected for critical analysis in this review.

RESULTS

These articles revealed ambulatory, non-invasive and wearable medical devices, such as the in-ear EEG devices; the accelerometer-based devices and the subcutaneous implanted EEG devices might be more acceptable than traditional EEG systems. In addition, extracerebral signalbased devices may be more efficient than EEG-based systems, especially when combined with an intervention trigger. Although further studies may still be required to improve and validate these proposed systems before commercialization, these findings may give hope to epileptic patients, particularly those with refractory epilepsy, to predict and manage their seizures.

CONCLUSION

The use of medical devices for epilepsy may improve patients' independence and quality of life and possibly prevent sudden unexpected death in epilepsy (SUDEP).

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG

OBJECTIVE

When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or even outdoor environment, one of the major concerns is varying nature of characteristics of different artifacts in time, frequency and spatial domains, which in turn causes a simple approach to be not enough for reliable artifact removal. Considering this, current study aims to use correlation-driven mapping to improve artifact detection performance.

APPROACH

A framework is proposed here for mapping signals from multichannel space (regardless of the number of EEG channels) into two-dimensional RGB space, in which the correlation of all EEG channels is simultaneously taken into account, and a deep convolutional neural network (CNN) model can then learn specific patterns in generated 2D representation related to specific artifact.

MAIN RESULTS

The method with a classification accuracy of 92.30% (AUC = 0.96) in a leave-three-subjects-out cross-validation procedure was evaluated using data including 2310 EEG sequences contaminated by artifacts and 2285 artifact-free EEG sequences collected with BrainStatus self-adhesive electrode and wireless amplifier from 15 intensive care patients. For further assessment, several scenarios were also tested including performance variation of proposed method under different segment lengths, different numbers of isoline and different numbers of channel. The results showed outperformance of CNN fed by correlation coefficients data over both spectrogram-based CNN and EEGNet on the same dataset.

SIGNIFICANCE

This study showed the feasibility of utilizing correlation image of EEG channels coupled with deep learning as a promising tool for dimensionality reduction, channels fusion and capturing various artifacts patterns in temporal-spatial domains. A simplified version of proposed approach was also shown to be feasible in real-time application with latency of 0.0181 s for making real-time decision.

Automatic detection of artifacts in EEG by combining deep learning and histogram contour processing

This paper introduces a simple approach combining deep learning and histogram contour processing for automatic detection of various types of artifact contaminating the raw electroencephalogram (EEG). The proposed method considers both spatial and temporal information of raw EEG, without additional need for reference signals like ECG or EOG. The proposed method was evaluated with data including 785 EEG sequences contaminated by artifacts and 785 artifact-free EEG sequences collected from 15 intensive care patients. The obtained results showed an overall accuracy of 0.98, representing high reliability of proposed technique in detecting different types of artifacts and being comparable or outperforming the approaches proposed earlier in the literature.

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful "emotional" interactions between humans and computers, the need for reliable and deployable solutions for the identification of human emotional states is required. Recent developments in using electroencephalography (EEG) for emotion recognition have garnered strong interest from the research community as the latest developments in consumer-grade wearable EEG solutions can provide a cheap, portable, and simple solution for identifying emotions. Since the last comprehensive review was conducted back from the years 2009 to 2016, this paper will update on the current progress of emotion recognition using EEG signals from 2016 to 2019. The focus on this state-of-the-art review focuses on the elements of emotion stimuli type and presentation approach, study size, EEG hardware, machine learning classifiers, and classification approach. From this state-of-the-art review, we suggest several future research opportunities including proposing a different approach in presenting the stimuli in the form of virtual reality (VR). To this end, an additional section devoted specifically to reviewing only VR studies within this research domain is presented as the motivation for this proposed new approach using VR as the stimuli presentation device. This review paper is intended to be useful for the research community working on emotion recognition using EEG signals as well as for those who are venturing into this field of research.

A walk in the park? Characterizing gait-related artifacts in mobile EEG recordings

Brain activity during natural walking outdoors can be captured using mobile electroencephalography (EEG). However, EEG recorded during gait is confounded with artifacts from various sources, possibly obstructing the interpretation of brain activity patterns. Currently, there is no consensus on how the amount of artifact present in these recordings should be quantified, or is there a systematic description of gait artifact properties. In the current study, we expand several features into a seven-dimensional footprint of gait-related artifacts, combining features of time, time-frequency, spatial, and source domains. EEG of N = 26 participants was recorded while standing and walking outdoors. Footprints of gait-related artifacts before and after two different artifact attenuation strategies (after artifact subspace reconstruction (ASR) and after subsequent independent component analysis [ICA]) were systematically different. We also evaluated topographies, morphologies, and signal-to-noise ratios (SNR) of button-press event-related potentials (ERP) before and after artifact handling, to confirm gait-artifact reduction specificity. Morphologies and SNR remained unchanged after artifact attenuation, whereas topographies improved in quality. Our results show that the footprint can provide a detailed assessment of gait-related artifacts and can be used to estimate the sensitivity of different artifact reduction strategies. Moreover, the analysis of button-press ERPs demonstrated its specificity, as processing did not only reduce gait-related artifacts but ERPs of interest remained largely unchanged. We conclude that the proposed footprint is well suited to characterize individual differences in gait-related artifact extent. In the future, it could be used to compare and optimize recording setups and processing pipelines comprehensively.

A multi-day and multi-band dataset for a steady-state visual-evoked potential-based brain-computer interface

BACKGROUND

A steady-state visual-evoked potential (SSVEP) is a brain response to visual stimuli modulated at certain frequencies; it has been widely used in electroencephalography (EEG)-based brain-computer interface research. However, there are few published SSVEP datasets for brain-computer interface. In this study, we obtained a new SSVEP dataset based on measurements from 30 participants, performed on 2 days; our dataset complements existing SSVEP datasets: (i) multi-band SSVEP datasets are provided, and all 3 possible frequency bands (low, middle, and high) were used for SSVEP stimulation; (ii) multi-day datasets are included; and (iii) the EEG datasets include simultaneously obtained physiological measurements, such as respiration, electrocardiography, electromyography, and head motion (accelerator).

FINDINGS

To validate our dataset, we estimated the spectral powers and classification performance for the EEG (SSVEP) datasets and created an example plot to visualize the physiological time-series data. Strong SSVEP responses were observed at stimulation frequencies, and the mean classification performance of the middle frequency band was significantly higher than the low- and high-frequency bands. Other physiological data also showed reasonable results.

CONCLUSIONS

Our multi-band, multi-day SSVEP datasets can be used to optimize stimulation frequencies because they enable simultaneous investigation of the characteristics of the SSVEPs evoked in each of the 3 frequency bands, and solve session-to-session (day-to-day) transfer problems by enabling investigation of the non-stationarity of SSVEPs measured on different days. Additionally, auxiliary physiological data can be used to explore the relationship between SSVEP characteristics and physiological conditions, providing useful information for optimizing experimental paradigms to achieve high performance.

An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299

Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p > 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (<40 Hz) EEG recording.

Low-complexity hardware design methodology for reliable and automated removal of ocular and muscular artifact from EEG

BACKGROUND AND OBJECTIVE

EEG is a non-invasive tool for neuro-developmental disorder diagnosis and treatment. However, EEG signal is mixed with other biological signals including Ocular and Muscular artifacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners which may result in less accurate diagnosis. Many existing methods require reference electrodes, which will create discomfort to the patient/children and cause hindrance to the diagnosis of the neuro-developmental disorder and Brain Computer Interface in the pervasive environment. Therefore, it would be ideal if these artifacts can be removed real time on the hardware platform in an automated fashion and then the denoised EEG can be used for online diagnosis in a pervasive personalized healthcare environment without the need of any reference electrode.

METHODS

In this paper we propose a reliable, robust and automated methodology to solve the aforementioned problem. The proposed methodology is based on the Haar function based Wavelet decompositions with simple threshold based wavelet domain denoising and artifacts removal schemes. Subsequently hardware implementation results are also presented. 100 EEG data from Physionet, Klinik für Epileptologie, Universität Bonn, Germany, Caltech EEG databases and 7 EEG data from 3 subjects from University of Southampton, UK have been studied and nine exhaustive case studies comprising of real and simulated data have been formulated and tested. The proposed methodology is prototyped and validated using FPGA platform.

RESULTS

Like existing literature, the performance of the proposed methodology is also measured in terms of correlation, regression and R-square statistics and the respective values lie above 80%, 79% and 65% with the gain in hardware complexity of 64.28% and improvement in hardware delay of 53.58% compared to state-of-the art approaches. Hardware design based on the proposed methodology consumes 75 micro-Watt power.

CONCLUSIONS

The automated methodology proposed in this paper, unlike the state of the art methods, can remove blink and muscular artifacts real time without the need of any extra electrode. Its reliability and robustness is also established after exhaustive simulation study and analysis on both simulated and real data. We believe the proposed methodology would be useful in next generation personalized pervasive healthcare for Brain Computer Interface and neuro-developmental disorder diagnosis and treatment.

Inhibition Underlies the Effect of High Need for Closure on Cultural Closed-Mindedness under Mortality Salience

The hypothesis that people respond to reminders of mortality with closed-minded, ethnocentric attitudes has received extensive empirical support, largely from research in the Terror Management Theory (TMT) tradition. However, the basic motivational and neural processes that underlie this effect remain largely hypothetical. According to recent neuropsychological theorizing, mortality salience (MS) effects on cultural closed-mindedness may be mediated by activity in the behavioral inhibition system (BIS), which leads to passive avoidance and decreased approach motivation. This should be especially true for people motivated to avoid unfamiliar and potentially threatening stimuli as reflected in a high need for closure (NFC). In two studies involving moderated mediation analyses, people high on trait NFC responded to MS with increased BIS activity (as indicated by EEG and the line bisection task), which is characteristic of inhibited approach motivation. BIS activity, in turn, predicted a reluctance to explore foreign cultures (Study 1) and generalized ethnocentric attitudes (Study 2). In a third study, inhibition was induced directly and caused an increase in ethnocentrism for people high on NFC. Moreover, the effect of the inhibition manipulation × NFC interaction on ethnocentrism was explained by increases in BIS-related affect (i.e., anxious inhibition) at high NFC. To our knowledge, this research is the first to establish an empirical link between very basic, neurally-instantiated inhibitory processes and rather complex, higher-order manifestations of intergroup negativity in response to MS. Our findings contribute to a fuller understanding of the cultural worldview defense phenomenon by illuminating the motivational underpinnings of cultural closed-mindedness in the wake of existential threat.

Methods for artifact detection and removal from scalp EEG: A review

Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications (only functional comparison is provided not performance evaluation of methods). Finally, the future direction and expected challenges of current research is discussed. Therefore, this review is expected to be helpful for interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research.

Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present.

Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application

In this paper, we propose a new unsupervised method to automatically characterize and detect events in multichannel signals. This method is used to identify artifacts in electroencephalogram (EEG) recordings of brain activity. The proposed algorithm has been evaluated and compared with a supervised method. To this end an example of the performance of the algorithm to detect artifacts is shown. The results show that although both methods obtain similar classification, the proposed method allows detecting events without training data and can also be applied in signals whose events are unknown a priori. Furthermore, the proposed method provides an optimal window whereby an optimal detection and characterization of events is found. The detection of events can be applied in real-time.

A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness

Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI) systems have been proposed to detect driver drowsiness. However, detecting driver drowsiness at its early stage poses a major practical hurdle when using existing BMI systems. This study proposes a context-aware BMI system aimed to detect driver drowsiness at its early stage by enriching the EEG data with the intensity of head-movements. The proposed system is carefully designed for low-power consumption with on-chip feature extraction and low energy Bluetooth connection. Also, the proposed system is implemented using JAVA programming language as a mobile application for on-line analysis. In total, 266 datasets obtained from six subjects who participated in a one-hour monotonous driving simulation experiment were used to evaluate this system. According to a video-based reference, the proposed system obtained an overall detection accuracy of 82.71% for classifying alert and slightly drowsy events by using EEG data alone and 96.24% by using the hybrid data of head-movement and EEG. These results indicate that the combination of EEG data and head-movement contextual information constitutes a robust solution for the early detection of driver drowsiness.

Isolating gait-related movement artifacts in electroencephalography during human walking

OBJECTIVE

High-density electroencephelography (EEG) can provide an insight into human brain function during real-world activities with walking. Some recent studies have used EEG to characterize brain activity during walking, but the relative contributions of movement artifact and electrocortical activity have been difficult to quantify. We aimed to characterize movement artifact recorded by EEG electrodes at a range of walking speeds and to test the efficacy of artifact removal methods. We also quantified the similarity between movement artifact recorded by EEG electrodes and a head-mounted accelerometer.

APPROACH

We used a novel experimental method to isolate and record movement artifact with EEG electrodes during walking. We blocked electrophysiological signals using a nonconductive layer (silicone swim cap) and simulated an electrically conductive scalp on top of the swim cap using a wig coated with conductive gel. We recorded motion artifact EEG data from nine young human subjects walking on a treadmill at speeds from 0.4 to 1.6 m s(-1). We then tested artifact removal methods including moving average and wavelet-based techniques.

MAIN RESULTS

Movement artifact recorded with EEG electrodes varied considerably, across speed, subject, and electrode location. The movement artifact measured with EEG electrodes did not correlate well with head acceleration. All of the tested artifact removal methods attenuated low-frequency noise but did not completely remove movement artifact. The spectral power fluctuations in the movement artifact data resembled data from some previously published studies of EEG during walking.

SIGNIFICANCE

Our results suggest that EEG data recorded during walking likely contains substantial movement artifact that: cannot be explained by head accelerations; varies across speed, subject, and channel; and cannot be removed using traditional signal processing methods. Future studies should focus on more sophisticated methods for removal of EEG movement artifact to advance the field.

Noise reduction in brainwaves by using both EEG signals and frontal viewing camera images

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been used in various applications, including human–computer interfaces, diagnosis of brain diseases, and measurement of cognitive status. However, EEG signals can be contaminated with noise caused by user's head movements. Therefore, we propose a new method that combines an EEG acquisition device and a frontal viewing camera to isolate and exclude the sections of EEG data containing these noises. This method is novel in the following three ways. First, we compare the accuracies of detecting head movements based on the features of EEG signals in the frequency and time domains and on the motion features of images captured by the frontal viewing camera. Second, the features of EEG signals in the frequency domain and the motion features captured by the frontal viewing camera are selected as optimal ones. The dimension reduction of the features and feature selection are performed using linear discriminant analysis. Third, the combined features are used as inputs to support vector machine (SVM), which improves the accuracy in detecting head movements. The experimental results show that the proposed method can detect head movements with an average error rate of approximately 3.22%, which is smaller than that of other methods.