Brain computer interface: control signals review

Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods*1

Electroencephalogram (EEG), boasting the advantages of portability, low cost, and high-temporal resolution, is a non-invasive brain-imaging modality that can be used to measure different brain states. However, EEG recordings are always contaminated with artifacts from different sources other than neurons, which renders EEG data analysis more difficult, and which potentially results in misleading findings. Therefore, it is essential for many medical and practical applications to remove these artifacts in the preprocessing stage before analyzing EEG data. In the last thirty years, various methods have been developed to remove different types of artifacts from contaminated EEG data; still though, there is no standard method that can be used optimally, and therefore, the research remains attractive as well as challenging. This paper presents an extensive overview of the existing methods for ocular, muscle, and cardiac artifact identification and removal with their comparative advantages and limitations. We also reviewed the schemes developed for validating the performances of algorithms with simulated and real EEG data. In future studies, researchers should focus not only on the combining of different methods with multiple processing stages for efficient removal of artifactual interferences but also on the development of standard criteria for validation of recorded EEG signals.

Identification and Removal of Physiological Artifacts From Electroencephalogram Signals: A Review

Optimizing the ICA-based removal of ocular EEG artifacts from free viewing experiments.

Steady state visual evoked potentials (SSVEPs)-based Brain-Computer interfaces (BCIs), as well as eyetracking devices, provide a pathway for re-establishing communication for people with severe disabilities. We fused these control techniques into a novel eyetracking/SSVEP hybrid system, which utilizes eye tracking for initial rough selection and the SSVEP technology for fine target activation. Based on our previous studies, only four stimuli were used for the SSVEP aspect, granting sufficient control for most BCI users. As Eye tracking data is not used for activation of letters, false positives due to inappropriate dwell times are avoided. This novel approach combines the high speed of eye tracking systems and the high classification accuracies of low target SSVEP-based BCIs, leading to an optimal combination of both methods. We evaluated accuracy and speed of the proposed hybrid system with a 30-target spelling application implementing all three control approaches (pure eye tracking, SSVEP and the hybrid system) with 32 participants. Although the highest information transfer rates (ITRs) were achieved with pure eye tracking, a considerable amount of subjects was not able to gain sufficient control over the stand-alone eye-tracking device or the pure SSVEP system (78.13% and 75% of the participants reached reliable control, respectively). In this respect, the proposed hybrid was most universal (over 90% of users achieved reliable control), and outperformed the pure SSVEP system in terms of speed and user friendliness. The presented hybrid system might offer communication to a wider range of users in comparison to the standard techniques.

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A Novel Hybrid Mental Spelling Application Based on Eye Tracking and SSVEP-Based BCI.

Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data.

https://www.mdpi.com/1424-8220/16/2/241/pdf

Hybrid EEG—Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal

Many efforts have been made to understand the neural mechanisms of the human brain. However, visualization of human brain processing has been a main challenge in the field. It is still largely unknown how the human brain allocates attention to target objects while excluding unrelated information in a complex visual environment. Using simultaneous electroencephalogram and eye tracking measurements, in this study, we analyzed two brain regions separately to detect the brain wave activity during visual information processing. We observed an activation difference between sensory (P100) and cognitive (P300) processing, and the behavioral response was improved by providing valid cue-target location information. Furthermore, neural processing was evaluated according to the specific area of brain activation and eye movements during cognitive processing. Our results demonstrate the correlation between behavior performance and visual stimuli and suggest an advantage of combined paradigms for efficient visua...

Shin-Jung Kim

Papers

Hybrid EEG—Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal

Synchronized tracking of brain cognitive processing using EEG and vision signals