Artifact Removal on An EEG-based BCI System

Brain-computer interface system, as a new technology in controlling and monitoring, has been drawing more and more attention. Brain-computer interface systems based on EEG signals, in particular, has been more and more popular since the technology of collecting EEG data is comparatively cheap and easy. However, due to the existence of various kinds of noises (artifacts), especially those from external origins such as power supply interference, and those from inner origins such as EOG and EMG, the analysis of EEG signals would be influenced and thus the BCI systems would produce false or inaccurate results. On the other hand, BCI systems also require real-time data flow that drives the controlling or monitoring system. In order to meet these two needs, this paper digs deep into the properties of EEG signals and artifacts, and proposes a novel online artifact removal algorithm that removes artifacts from EEG data flow in real-time. This algorithm is based on independent component analysis (ICA) and wavelet-denoising algorithms using the spatial and topological features of artifacts in the way of drawing component scalp maps. Results show that this algorithm is effective and computationally efficient in removing artifacts. Introduction A brain–computer interface (BCI), often called a mind-machine interface (MMI), or sometimes called a direct neural interface or a brain–machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions. Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. EEG is one of the mostly used and most objective physiology indicator for subjects mental activity and state, and is widely applied in BCI systems. For example, vigilance is a term which describes the ability of subjects to maintain their focus of attention and to remain alert to stimuli for a prolonged period of time. It is of great importance to be able to check if one, especially one who is operating heavy equipment or performing vital jobs such as flight control, is vigilant enough in order to avoid accidents and tragedies. When vigilance state changes, electroencephalograms (EEG) recorded from the person will also has some relative changes. Through decades of research, we are aware that some EEG signal components of certain rhythms are in some way correlated to human’s vigilance level. Often, different rhythms of EEG signals are divided into delta (up to 4 Hz), theta (4 to 7 Hz), alpha (8 to 12 Hz), beta (12 to 30 Hz), gamma (30 to 100 Hz), and mu (8 to 13 Hz). Their amplitude are correlated to all kinds of human activities. Therefore, we can estimate the vigilance by using EEG of subjects performing some tasks. EEG signals are often recorded by a set of equipments that consist of electrode cap, amplifier, voltmeter and signal collector. EEG signals are feeble compared to all kinds of noises and artifacts. Artifacts refer to the interference signals generated by the muscle activities, such as eye movement, heartbeat, activation of muscles that are near head, etc. (EOG, EKG, EMG induced artifacts, respectively)[1]. Also, there are white noises and noises from other nearby electrical appliances. fig.1 Delta, Theta, Alpha, Beta, Gamma rhythms fig.2 Electrode location map Lots of methods have been introduced and applied to remove artifacts. At first, artifacts are removed through traditional filtering. But traditional filtering methods have very limited capability, and they often induce a great loss of real EEG signals. The independent component analysis becomes the most popular way of artifact identification and removal in the past five years. Someone recently developed generalized morphological component analysis for artifact removal [2], which also seems to be a valid method. The technique of wavelet-denoising [13] is also a comparatively new method used in artifact removal. This technique is of comparatively low computational cost and high efficiency. Neurological Phenomenon in EEG There are some neurological phenomenon that are particularly of interest in BrainComputer Interface systems, including: • Changes in the brain rhythms such as Mu, Beta and Gamma rhythms related to a movement (CBR): A voluntary movement results in a circumscribed desynchronization in the Mu and lower Beta bands • Movement-related potentials (MRP): MRPs are low-frequency potentials that start about 1–1.5 s before a movement. • Other movement-related activities (OMRAs): The movement-related activities that do not belong to any of the preceding categories are categorized as OMRA. • Slow cortical potentials (SCPs): SCPs are slow non-movement potential changes generated by the user. They reflect changes in the cortical polarization of the EEG, lasting from 300 ms up to several seconds. • Cognitive tasks (CTs): Changes in the brain signals as a result of non-movement mental tasks • P300: Infrequent or particularly significant auditory, visual or somatosensory stimuli, when interspersed with frequent or routine stimuli, typically evoke a positive peak at about 300 ms after the stimulus is received • Visual evoked potentials (VEP): VEPs are small changes in the brain signal, generated in response to a visual stimulus such as flashing lights • Steady-State visual evoked potentials (SSVEP): If a visual stimulus is presented repetitively at a rate of 5–6 Hz or greater, a continuous oscillatory electrical response is elicited in the visual pathways. Such a response is termed SSVEP. • Auditory evoked potentials (AEPs): AEPs are small electrical activity changes that are generated in response to an auditory stimulus. • Somatosensory evoked potentials (SSEPs): SSEPs are potentials generated in response to the stimulation of somatic sensation. • Multiple neurological phenomena (MNs): BCI systems based on multiple neurological phenomena use a combination of two or more of the above-mentioned neurological phenomena. These phenomenon should be preserved in the artifact removal step. Due to the need for online artifact removal in applications such as BCI and online EEG evaluation, it is important to have an efficient yet still effective method for artifact detection and removal. One category of methods stand out among all the artifact removal methods in terms of close-to-real-time performance, which is ICA-based methods. There are lots of ICA methods available now, such as Infomax, FastICA[7], Second Order Blind Identification (SOBI) [15], and AMUSE [4]. Artifacts in EEG Artifacts in EEG signals usually originate from sources other than neurological activities in the brain. They could contaminate the EEG waves by modifying the shapes of neurological phenomenon waves, which could result in fallacious responses in BCI systems. A special case to this claim is that sometimes, irrelevant neurological phenomenon would be regarded as artifacts as well in some application. For example, a Cognitive Task based BCI system would treat SSVEP, a static neurological phenomenon, as artifact. These artifacts could incur errors when controlling devices. Thus, for each specific type of EEG application, it is necessary to avoid and remove artifacts. Artifacts could come from both physiological and non-physiological sources. Nonphysiological signals are from sources other than the human subject, such as the 50Hz mains power, or the contact resistance changes related to subjects movement. These could be avoided by proper filtering and other measures. Physiological signals are produced by a large variety of body activities. For example, ECG comes from heartbeat, which is a rhythmic signal that could be mixed in the collected EEG. Breathe could also result in rhythmic signals. Some changes on the scalp such as exuding could also give rise to artifacts that are related to changes in the resistance at the electrodes. There are two kinds of physiological artifacts that are widely studied, which are the signals that are related to eye movement (EOG) and that of muscle movement(EMG). EOG is often caused by eye-blinks, which is of high amplitude, and movement of eyeball, which is of low frequency. EOG consists of a wide spectrum, and it has a peak at around 4Hz, which is the major component in the activities on the forehead. EMG activities in EEG record are often caused by the movement of subjects’ head, body, lower jaw or tone. They also have wide spectrum, and they often peak at above 30Hz. In difficult tasks EMG activities are often increased because of more facial muscle movements. Studies have shown that EOG and EMG could affect the neurological phenomenon that are made use of in BCI systems. These two types of artifacts are more challenging to remove than nonphysiological artifacts due to their non-static wide-spectrum nature. There are three types of methods that can be used to treat artifacts, which are artifact avoidance, artifact exclusion and artifact removal[5]. To avoid occurrences of artifacts, proper instructions should be given to subjects, such as to avoid moving their body or eye-blinks during experiment. This is easy to perform, and the resulting EEG records are less compromised by movement-related artifacts. But this method could not deal with artifacts as a result of spontaneous physiological activities such as heartbeat and breathe. Even eye and muscle movement are harder to control that people would normally think. It’s also hard to collect a longer period of EEG record with artifact avoidance. When artifacts do occur, the window of EEG that’s contaminated by artifacts could be manually or automatically excluded by inspection on the wave shapes. This method takes the burden of dealing with artifacts from subjects which makes the experiments easier to carry out, especially for subjects with movement disorder such as patients with Parkinson’s disease. Manu