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Human Computer Interface Ppt [HOT] Download

Brain-computer interface (BCI) paradigms are usually tested when environmental and biological artifacts are intentionally avoided. In this study, we deliberately introduced different perturbations in order to test the robustness of a steady state visual evoked potential (SSVEP) based BCI. Specifically we investigated to what extent a drop in performance is related to the degraded quality of EEG signals or rather due to increased cognitive load. In the online tasks, subjects focused on one of the four circles and gave feedback on the correctness of the classification under four conditions randomized across subjects: Control (no perturbation), Speaking (counting loudly and repeatedly from one to ten), Thinking (mentally counting repeatedly from one to ten), and Listening (listening to verbal counting from one to ten). Decision tree, Naïve Bayes and K-Nearest Neighbor classifiers were used to evaluate the classification performance using features generated by canonical correlation analysis. During the online condition, Speaking and Thinking decreased moderately the mean classification accuracy compared to Control condition whereas there was no significant difference between Listening and Control conditions across subjects. The performances were sensitive to the classification method and to the perturbation conditions. We have not observed significant artifacts in EEG during perturbations in the frequency range of interest except in theta band. Therefore we concluded that the drop in the performance is likely to have a cognitive origin. During the Listening condition relative alpha power in a broad area including central and temporal regions primarily over the left hemisphere correlated negatively with the performance thus most likely indicating active suppression of the distracting presentation of the playback. This is the first study that systematically evaluates the effects of natural artifacts (i.e. mental, verbal and audio perturbations) on SSVEP-based BCIs. The results can be used to improve individual classification performance taking into account effects of perturbations.

Human Computer Interface Ppt Download


Citation: İşcan Z, Nikulin VV (2018) Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations. PLoS ONE 13(1): e0191673.

Brain-computer interfaces (BCIs) have potential to help severely disabled people by translating the intentions of subjects into a number of different commands [1]. Due to its safety and high time resolution, electroencephalogram (EEG) based BCIs have become popular and various designs using different signals (e.g. P300 [2,3], sensorimotor rhythms [4,5]) have been proposed. Among them, steady state visual evoked potentials (SSVEPs) are particularly attractive due to high signal to noise ratio (SNR) [6] and robustness [7]. SSVEP is a resonance phenomenon which can be observed mainly in electrodes over the occipital and parietal lobes of brain when a subject looks at a light source flickering at a constant frequency [7]. In this case, there is an increase in the amplitude of the EEG at flickering frequencies and their harmonics and there are different methods to extract the frequency components of SSVEPs. Recently, canonical correlation analysis (CCA) has become a popular approach for analyzing these frequency components as its performance was higher compared to traditionally used Fourier transform [8] and minimum energy combination [9]. Several extensions to standard CCA method were proposed and their performances were evaluated [10].

The Human-Computer Interface (HCI) deals with the methods by which computers and their users communicate. It is the process of designing interface software so that computers are pleasant, easy to use and do what people want them to do. Dealing with HCI requires the study of not only the hardware of the computer, but that of the human side also. Therefore attention must be paid to human psychology and physiology. This is because to build a better two-way communication, one must know the capabilities and limitation of both sides. This seminar also deals with concepts and guidelines that should be followed in order to produce a good HCI. Specifically dealt with topics include Dialogue Design, Presentation Design, General Input and Output.

CONCLUSIONContinuation and acceleration of recent progress inBCI research and development requires increased focus on theproduction of peer-reviewed research articles in high qualityjournals, identification and widespread presentations and frommedia attention. For the near future, research funding will dependprimarily on public agencies and private foundations that fundresearch directed at the needs of those with severe motordisabilities. With further increases in speed, accuracy, and rangeof applications, BCI technology could become applicable to largerpopulations and could thereby engage the interest and resources ofprivate industry.REFERENCES1. Niels Birbaumer, P. Hunter Backham,Brain Computer Interface Technology: A Review of FirstInternational Meeting, IEEE Transactions on RehabilitationEngineering, Vol.8, No.2, June 2000.2. Anirudh Vallabhaneni, TaoWang, Brain Computer Interface, University of Illinois, Chicago,2005.3. Haider Hussein Alwaiti, Ishak Aris, Brain ComputerInterface Design & Applications: Challenges & Future, WorldApplied Journal 11, 2010.4. Jan B. F. Vanerp, Fabien Lotte,Brain-Computer Interfaces for Non-Medical Applications: How to MoveForward, Computer-IEEE Computer Society-45, April 2012.5. 076b4e4f54


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