This paper presents a systematic review of electroencephalography (EEG)-based brain---computer interfaces (BCIs) used in the video games, a vibrant field of research that touches upon all relevant questions concerning the future directions of BCI. The paper examines the progress of BCI research with regard to games and shows that gaming applications offer numerous advantages by orienting BCI to concerns and expectations of a gaming application. Different BCI paradigms are investigated, and future directions are discussed.

EEG-based BCI and video games: a progress report

The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrum-based channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also can achieve significantly better classification accuracies for MI-based BCI (Wilcoxon signed test, p < 0.05).

/pdf/bispectrum-based-channel-selection-for-motor-imagery-based-299irdfwhv.pdf

Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing

The paper proposes a novel approach toward EEG-driven position control of a robot arm by utilizing motor imagery, P300 and error-related potentials (ErRP) to align the robot arm with desired target position. In the proposed scheme, the users generate motor imagery signals to control the motion of the robot arm. The P300 waveforms are detected when the user intends to stop the motion of the robot on reaching the goal position. The error potentials are employed as feedback response by the user. On detection of error the control system performs the necessary corrections on the robot arm. Here, an AdaBoost-Support Vector Machine (SVM) classifier is used to decode the 4-class motor imagery and an SVM is used to decode the presence of P300 and ErRP waveforms. The average steady-state error, peak overshoot and settling time obtained for our proposed approach is 0.045, 2.8 % and 44 s, respectively, and the average rate of reaching the target is 95 %. The results obtained for the proposed control scheme make it suitable for designs of prosthetics in rehabilitative applications.

Motor imagery, P300 and error-related EEG-based robot arm movement control for rehabilitation purpose.

https://pure.ulster.ac.uk/ws/files/78998815/Interval_Type_2_Fuzzy_Logic_based_Multiclass_ANFIS_Algorithm_for_Real_Time_EEG_based_Movement_Control_of_a_Robot_Arm_Final_Version.pdf

Interval type-2 fuzzy logic based multiclass ANFIS algorithm for real-time EEG based movement control of a robot arm

CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI

Electroencephalogram (EEG) signals generated out of motor imagery (MI) can be used for Brain Computer Interfacing (BCI). In order to accomplish this goal, we have classified four different MI tasks using two hybrid features of bispectrum of EEG through a RBF kernel support vector machine. As a demonstration of its applicability for a non-invasive BCI, we design and develop a BCI maze game, where a player plays the game in real time using his brain signals.

Motor imagery based BCI for a maze game

Of late, several studies have established the applicability of bispectrum technique for EEG signal analysis This paper explores hybrid features of bispectrum for classification of motor imagery (MI) Four different MI is classified based two hybrid features of bispectrum through a RBF kernel support vector machine

Classification of motor imagery based on hybrid features of bispectrum of EEG

Ambient Intelligence (AmI) is about an intelligent environment being aware of the human that interacts with it. Brain Computer Interface (BCI) is part of physiological computing wherein signals from the brain is made to control external devices. One of the major concerns within AmI is to address the issue of user adaptability through a range of intelligent sensors so as to assist the human proactively in his activities and achieving his goals. Towards this end, BCI can be seen as an intelligent sensor for Ambient Intelligent Living. This position paper introduces work being carried out at Tezpur University in pursuit of these goals. Keywords— brain computer interface, intelligent assistive system, intelligent sensor. motor control commands, either directly to control a prosthetic limb or deliver natural motor control of muscles via functional electrical stimulation. BCI could be invasive and non-invasive. Non-invasive approaches are preferred over invasive ones. Within noninvasive BCI, researchers have investigated use of Magneto Encephalography [4], Near Infrared Spectroscopy [5], functional Magnetic Resonance Imaging [6] to measure brain activity. The most widely used non-invasive BCI is by far based on EEG signals [7]. Traditionally BCI research has largely been aimed at users with severe motor impairments, enabling them to interact with the outside world.

Simanta Bordoloi

Papers

Motor imagery based BCI for a maze game

Classification of motor imagery based on hybrid features of bispectrum of EEG

Brain Computer Interface as Sensor for Ambient Intelligent Living: A Position Paper