Simulation of Wheelchair Control by Integration of Computers and Electronics Platform in BCI Controlled Systems

Device control using brain signals is a technique which could dramatically change the life of disabled individuals. An attempt has been made to control such devices using brain signals. This work shows the hardware implementation of controlling a wheelchair using brain signals which is developed using electronics open source platforms to simulate real wheelchair control. This system uses Arduino UNO microcontroller board, L293D driver circuit, two DC motors, wheelchair hardware and wires for connections to produce simulation of wheelchair control

Real-time SSVEP measurements through Lock-in detection in FPGA-based platform

In this work, a method for measuring steady-state visually evoked potentials using the Lock-In technique is presented. The proposed method in- volves acquiring the electroencephalography signal through channel averaging from an ADS1299 sigma-delta converter, without the need for additional hardware to accommodate the signal and processing in real-time using an Intel MAX10 FPGA, while visual stimuli synchronized with the sampling and pro- cessing are generated. The result is a robust platform that allows determining a user's attention focus on visual stimuli flickering at 14.70, 16.67, and 19.23 Hz. The initial experimental tests of the system with three subjects validated the platform, obtaining an average signal-to-noise ratio of 3.2 in the detection, with a maximum of 6.2 in the case of an experienced SSVEP user.Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señale

A hybrid environment control system combining EMG and SSVEP signal based on brain-computer interface technology

The patients who are impaired with neurodegenerative disorders cannot command their muscles through the neural pathways. These patients are given an alternative from their neural path through Brain-Computer Interface (BCI) systems, which are the explicit use of brain impulses without any need for a computer's vocal muscle. Nowadays, the steady-state visual evoked potential (SSVEP) modality offers a robust communication pathway to introduce a non-invasive BCI. There are some crucial constituents, including window length of SSVEP response, the number of electrodes in the acquisition device and system accuracy, which are the critical performance components in any BCI system based on SSVEP signal. In this study, a real-time hybrid BCI system consists of SSVEP and EMG has been proposed for the environmental control system. The feature in terms of the common spatial pattern (CSP) has been extracted from four classes of SSVEP response, and extracted feature has been classified using K-nearest neighbors (k-NN) based classification algorithm. The obtained classification accuracy of eight participants was 97.41%. Finally, a control mechanism that aims to apply for the environmental control system has also been developed. The proposed system can identify 18 commands (i.e., 16 control commands using SSVEP and two commands using EMG). This result represents very encouraging performance to handle real-time SSVEP based BCI system consists of a small number of electrodes. The proposed framework can offer a convenient user interface and a reliable control method for realistic BCI technology

Study of non-invasive cognitive tasks and feature extraction techniques for brain-computer interface (BCI) applications

A brain-computer interface (BCI) provides an important alternative for disabled people that enables the non-muscular communication pathway among individual thoughts and different assistive appliances. A BCI technology essentially consists of data acquisition, pre-processing, feature extraction, classification and device command. Indeed, despite the valuable and promising achievements already obtained in every component of BCI, the BCI field is still a relatively young research field and there is still much to do in order to make BCI become a mature technology. To mitigate the impediments concerning BCI, the study of cognitive task together with the EEG feature and classification framework have been investigated. There are four distinct experiments have been conducted to determine the optimum solution to those specific issues. In the first experiment, three cognitive tasks namely quick math solving, relaxed and playing games have been investigated. The features have been extracted using power spectral density (PSD), logenergy entropy, and spectral centroid and the extracted feature has been classified through the support vector machine (SVM), K-nearest neighbor (K-NN), and linear discriminant analysis (LDA). In this experiment, the best classification accuracy for single channel and five channel datasets were 86% and 91.66% respectively that have been obtained by the PSD-SVM approach. The wink based facial expressions namely left wink, right wink and no wink have been studied through fast Fourier transform (FFT) and sample range feature and then the extracted features have been classified using SVM, K-NN, and LDA. The best accuracy (98.6%) has been achieved by the sample range-SVM based approach. The eye blinking based facial expression has been investigated following the same methodology as the study of wink based facial expression. Moreover, the peak detection approach has also been employed to compute the number of blinks. The optimum accuracy of 99% has been achieved using the peak detection approach. Additionally, twoclass motor imagery hand movement has been classified using SVM, K-NN, and LDA where the feature has been extracted through PSD, spectral centroid and continuous wavelet transform (CWT). The optimum 74.7% accuracy has been achieved by the PSDSVM approach. Finally, two device command prototypes have been designed to translate the classifier output. One prototype can translate four types of cognitive tasks in terms of 5 watts four different colored bulbs, whereas, another prototype may able to control DC motor utilizing cognitive tasks. This study has delineated the implementation of every BCI component to facilitate the application of brainwave assisted assistive appliances. Finally, this thesis comes to the end by drawing the future direction regarding the current issues of BCI technology and these directions may significantly enhance usability for the implementation of commercial applications not only for the disabled but also for a significant number of healthy users

Brain computer interfaces: an engineering view. Design, implementation and test of a SSVEP-based BCI.

This thesis presents the realization of a compact, yet flexible BCI platform, which, when compared to most commercially-available solution, can offer an optimal trade-off between the following requirements: (i) minimal, easy experimental setup; (ii) flexibility, allowing simultaneous studies on other bio-potentials; (iii) cost effectiveness (e.g. < 1000 €); (iv) robust design, suitable for operation outside lab environments. The thesis encompasses all the project phases, from hardware design and realization, up to software and signal processing. The work started from the development of the hardware acquisition unit. It resulted in a compact, battery-operated module, whose medium-to-large scale production costs are in the range of 300 €. The module features 16 input channels and can be used to acquire different bio-potentials, including EEG, EMG, ECG. Module performance is very good (RTI noise < 1.3 uVpp), and was favourably compared against a commercial device (g.tec USBamp). The device was integrated into an ad-hoc developed Matlab-based platform, which handles the hardware control, as well as the data streaming, logging and processing. Via a specifically developed plug-in, incoming data can also be streamed to a TOBI-interface compatible system. As a demonstrator, the BCI was developed for AAL (Ambient Assisted Living) system-control purposes, having in mind the following requirements: (i) online, self-paced BCI operation (i.e., the BCI monitors the EEG in real-time and must discern between intentional control periods, and non-intentional, rest ones, interpreting the user’s intent only in the first case); (ii) calibration-free approach (“ready-to-use”, “Plug&Play”); (iii) subject-independence (general approach). The choice of the BCI operating paradigm fell on Steady State visual Evoked Potential (SSVEP). Two offline SSVEP classification algorithms were proposed and compared against reference literature, highlighting good performance, especially in terms of lower computational complexity. A method for improving classification accuracy was presented, suitable for use in online, self-paced scenarios (since it can be used to discriminate between intentional control periods and non-intentional ones). Results show a very good performance, in particular in terms of false positives immunity (0.26 min^-1), significantly improving over the state of the art. The whole BCI setup was tested both in lab condition, as well as in relatively harsher ones (in terms of environmental noise and non-idealities), such as in the context of the Handimatica 2014 exhibition. In both cases, a demonstrator allowing control of home appliances through BCI was developed