Quantification of SSVEP responses using multi-chromatic LED stimuli: Analysis on colour, orientation and frequency

Most LED visual stimulators used in steady state visual evoked potential (SSVEP) brain-computer interface (BCI) use single LED sources to elicit SSVEP responses. In this study, we tested the hypothesis that different orientations would have different responses in different participants and aimed to develop a portable LED based stimulus design which consists of a small number of RGB LEDs arranged in a line which can be oriented horizontally or vertically. The colour and frequency of the flicker were controlled by a portable microcontroller platform. The study investigated the performance of the SSVEP from five participants when the LED stimulus was displayed vertically and horizontally for a period of 30 seconds. The frequency range used was from 7 Hz to 11 Hz with three primary colours: red, green and blue in both orientations. Furthermore, we also compared the effect of vertical and horizontal orientations using four different frequencies and three colours to test visual fatigue reduction. The results of the analysis using band-pass filtering and Fast Fourier Transform showed that the green horizontal LED stimulus orientation gave the highest response and viewing comfort in all the participants rather than the vertical orientation

SSVEP-based brain-computer interface for computer control application using SVM classifier

n this research, a Brain Computer Interface (BCI) based on Steady State Visually Evoked Potential (SSVEP) for computer control appli-cations using Support Vector Machine (SVM) is presented. For many years, people have speculated that electroencephalographic activi-ties or other electrophysiological measures of brain function might provide a new non-muscular channel that can be used for sending messages or commands to the external world. BCI is a fast-growing emergent technology in which researchers aim to build a direct channel between the human brain and the computer. BCI systems provide a new communication channel for disabled people. Among many different types of the BCI systems, the SSVEP based has attracted more attention due to its ease of use and signal processing. SSVEPs are usually detected from the occipital lobe of the brain when the subject is looking at a twinkling light source. In this paper, SVM is used to classify SSVEP based on electroencephalogram data with proper features. Based on the experiment utilizing a 14-channel Electroencephalography (EEG) device, 80 percent of accuracy can be reached by our SSVEP-based BCI system using Linear SVM Kernel as classification engine

DESIGN OF PORTABLE LED VISUAL STIMULUS AND SSVEP ANALYSIS FOR VISUAL FATIGUE REDUCTION AND IMPROVED ACCURACY

Brain-computer interface (BCI) applications have emerged as an innovative communication channel between computers and human brain as it circumvents peripheral limbs thereby creating a direct interface between brain thoughts and the external world. This research focuses on non-invasive BCI to improve the design of visual stimuli in eliciting steady-state visual evoked potential (SSVEP) for BCI applications. To evoke SSVEP in the brain, the user needs to focus on a visual stimulus flickering at a constant frequency. Traditionally in research studies, the visual stimulus for SSVEP uses LCD screens where the flicker is generated using black or white patterns, which alternates the colour to produce a flickering effect. However, there are drawbacks for LCD based visual stimuli systems that limit the user acceptance of SSVEP applications. The main limitations are: (i) choice of flicker frequency is limited to the LCDs vertical refresh rate (ii) flickers are mainly limited to black/white patterns (iii) higher visual fatigue for the user due to LCDs background flicker (iv) reduced visual stimulus portability (v) Inaccurate flickers generated and controlled by the software (vi) influence of adjacent flickers causing attention shift when multiple flickers are used for classification and also not being easily adaptable for user requirements. The impediments in eliciting and utilising SSVEP responses for designing a near real-time platform for controlling external applications are addressed from five main perspectives here: (i) design of standalone LED visual stimulus hardware for precise generation of any frequency for replacing the LCD based visual stimulus (ii) eliciting maximal response by choosing most responsive colour, orientation and shape of visual stimulus (iii) identification of the best luminance level for visual stimulus to improve the comfortability of the user and for improved SSVEP response (iv) control of the duration of ON/OFF period for the visual stimulus to reduce eyestrain for the user (i.e. visual fatigue), and (v) hybrid BCI paradigm using SSVEP and P300 to improve the classification accuracy for controlling external applications. The experimental study involved the development of various visual stimulus designs based on LEDs and microcontrollers to minimise the visual fatigue and improve the SSVEP responses. The signal analysis results from the studies with five to ten participants show SSVEP elicitation is influenced by colour, orientation, the shape of stimulus, the luminance level of stimulus and the duration of ON/OFF period for the stimulus. The participants also commented that choosing the correct luminance and ON/OFF periods of the stimulus considerably reduce the eyestrain, improve the attention levels and reduce the visual fatigue. Taken together, these finding leads to more user acceptance in SSVEP based BCI as an assistive mechanism for controlling external applications with improved comfort, portability and reduced visual fatigue