Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

/pdf/current-status-challenges-and-possible-solutions-of-eeg-4el4wek5td.pdf

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

A review of disability EEG based wheelchair control system: Coherent taxonomy, open challenges and recommendations.

I: Wind Data Analysis.- 1. Introduction.- 1.1. Surface Wind Observation.- 1.2. General Weather Pattern.- 1.3. Outline of this Monograph.- 2. The Initial Decomposition.- 2.1. General Background.- 2.2. Robust Filtering.- 2.3. Univariate Filter Study.- 2.4. Multivariate Filter Study.- 2.5. Application to Wind Series.- 2.6. Appendix: Mathematical Details.- 3. The Geostrophic Component.- 3.1. The Geostrophic Wind.- 3.2. Estimation of the Geostrophic Wind.- 3.3. Comparison with the Geostrophic Component.- 3.4. Synoptic States.- 3.5. Appendix: Derivation of the Geostrophic Wind Equation.- 4. The Land and Sea Breeze Cycle.- 4.1. The Nature of the Circulation.- 4.2. Statistical Approach.- 4.3. Land and Sea Breeze Pattern.- 5. Short-Term Events.- 5.1. Meteorological Patterns.- 5.2. Wind Classification.- 5.3. Characteristics of Short-Term Events.- 5.4. Appendix: Removal of Storms.- II: Time Series of Directional Data.- 6. Time Series Models for Directional Data.- 6.1. Circular Variables.- 6.2. The von Mises Process.- 6.3. The Wrapped Autoregressive Process.- 7. Measures of Angular Association.- 7.1. Desirable Properties.- 7.2. Bivariate Angular Distributions.- 7.3. Review of Measures of Association.- 7.4. A Proposal for Vector Valued Time Series.- 7.5. Appendix: Non-von Mises Marginals.- 8. Comparison of Different Measures of Association.- 8.1. Independent Bivariate Directional Data.- 8.2. Time Series of Directional Data.- 9. Inference from the Wrapped Autoregressive Process.- 9.1. Introduction.- 9.2. Equating Theoretical and Empirical Circular Variance (EQ).- 9.3. Corrected EQ-Estimation (EC).- 9.4. Bayes Estimation (BA).- 9.5. Maximum Likelihood Estimation (ML).- 9.6. Characteristic Function Estimation (CF).- 9.7. Numerical Comparison of Estimators.- 10. Application to Series of Residual Wind Directions.- 11. Conclusions and Summary of Results.- List of Symbols.

The Analysis of Directional Time Series: Applications to Wind Speed and Direction

This project is a smart wheelchair based on eye tracking which is designed for people with locomotor disabilities. The add-on controlled module can be used with any electrical wheelchair. The smart wheel chair consists of four modules including imaging processing module, wheelchair-controlled module, SMS manager module and appliance-controlled module. The image processing module comprises of a webcam installed on the eyeglass and C++ customized image processing software. The captured image which is transmitted to raspberry Pi microcontroller will be processed using OpenCV to derive the 2D direction of eye ball. The coordinate of eyeball movement is then wirelessly transmitted to wheelchair-controlled module to control the movement of wheel chair. The wheelchair-controlled module is two dimensional rotating stages that installed to the joystick of the electrical wheelchair to replace the manual control of the wheelchair. The motion of eyeball is also used as the cursor control on the raspberry Pi screen to control the operation of some equipped appliance and send message to smart phone.

Smart wheelchair based on eye tracking

Kernel-based extreme learning machine (KELM), as a natural extension of ELM to kernel learning, has achieved outstanding performance in addressing various regression and classification problems. Compared with the basic ELM, KELM has a better generalization ability owing to no needs of the number of hidden nodes given beforehand and random projection mechanism. Since KELM is derived under the minimum mean square error (MMSE) criterion for the Gaussian assumption of noise, its performance may deteriorate under the non-Gaussian cases, seriously. To improve the robustness of KELM, this article proposes a mixture correntropy-based KELM (MC-KELM), which adopts the recently proposed maximum mixture correntropy criterion as the optimization criterion, instead of using the MMSE criterion. In addition, an online sequential version of MC-KELM (MCOS-KELM) is developed to deal with the case that the data arrive sequentially (one-by-one or chunk-by-chunk). Experimental results on regression and classification data sets are reported to validate the performance superiorities of the new methods. 

Mixture Correntropy-Based Kernel Extreme Learning Machines

In this study we try to explore the new channel of communication i.e. building a brain computer interface (BCI). The aim of this BCI is to provide solutions to facilitate and increase interaction between an ALS patient and his environment, using the strategies, techniques, and technologies most suitable to meet the user's needs for communication. We try to build an EEG-based BCI to help ALS patients with lack of focused attention perform their everyday activities in a less dependent manner. The next goal will be to enable them to move their wheelchair in the direction they want or to guide them to their destination.

EEG-controlled Wheelchair for ALS Patients

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EEG-controlled Wheelchair for ALS Patients