Development of a Low-Cost FPGA-Based SSVEP BCI Multimedia Control System

Shyu, Kuo-Kai; Lee, Po-Lei; Lee, Ming-Huan; Lin, Ming-Hong; Lai, R.H.; Chiu, Yun-Jen

doi:10.1109/tbcas.2010.2042595

Cited by 80 publications

(40 citation statements)

References 23 publications

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“…Most of the literature in which a full BCI system is implemented are concentrated on the steady-state visual Evoked Potential, which is an EEG signal response to the ickering visual stimulus [44], [45], [46]. However, in [47], a BCI system is implemented based on motor imagery, which includes Finite Impulse Response lter as a preprocessing, CSP as a feature extraction, and Mahalanobis distance as a classi cation, on Stratix IV FPGA Board with operational frequency of 200 MHz.…”

Section: Previous Work On Hardwarementioning

confidence: 99%

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An Efficient Hardware Implementation for a Motor Imagery Brain Computer Interface System

et al. 2018

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Abstract. Brain Computer Interface (BCI) systems, which are based on motor imagery, enable humans to command arti cial peripherals by merely thinking about the task. There is a tremendous interest in implementing BCIs on portable platforms, such as Field Programmable Gate Arrays (FPGAs) due to their low-cost, low-power and portability characteristics. This article presents the design and implementation of a Brain Computer Interface (BCI) system based on motor imagery on a Virtex-6 FPGA. In order to design an accurate algorithm, the proposed method avails statistical learning methods such as Mutual Information (MI), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM). It also uses Separable Common Spatio Spectral Pattern (SCSSP) method in order to extract features. Simulation results prove achieved performances of 73.54% for BCI competition III-dataset V, 67.2% for BCI competition IV-dataset 2a with all four classes, 80.55% for BCI competition IV-dataset 2a with the rst two classes, and 81.9% for captured signals. Moreover, the nal reported hardware resources determine its e ciency as a result of using retiming and folding techniques from the VLSI architecture' perspective. The complete proposed BCI system achieves not only excellent recognition accuracy, but also remarkable implementation e ciency in terms of portability, power, time, and cost.

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Section: Previous Work On Hardwarementioning

confidence: 99%

“…For example, Wearable Mobile EEG-based Brain Computer Interface System (WMEBCIS) [49] is proposed for detecting drowsiness. A low-cost FPGA-based BCI multimedia control system is also proposed in [50]. Their proposed framework is used to control multimedia devices.…”

Section: Previous Work On Hardwarementioning

confidence: 99%

An Efficient Hardware Implementation for a Motor Imagery Brain Computer Interface System

et al. 2018

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“…The advantages of this study are they can maximize is the data flow, large-scale operation, and efficient data classification. For brain-computer interface [5] this work maybe one of the most notable work ever done. TOP design paradigm for connecting the brain with the computer through that page in a very cheapest way.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Novel Complete Filter for EEG Application on FPGA

Mahabub¹

2018

IJIGSP

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Abstract-Filter is vastly used to detect different human signal in real time. In this paper, a novel complete digital filter is proposed for the fast detection of EEG signals due to avoid the mixtures of different biomedical signals. This paper intends to design a digital complete filter based on Field Programmable Gate Array (FPGA) for the alleviation of unwanted frequency components in biomedical signals specially EEG signals. For this purpose, complete filter which is a combination of integrator filter and differentiator filter which supports both low and high noises and comparatively inexpensive than other signal processing methodologies can be used. For hardware implementation, FPGA board is used which is a combination of different logic gates which offers inexpensive and long lasting services.

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“…It is further used for executing desired instructions by the user to the computing system for investigation, analysis of the desire and emotional state of the user. HCI applications like eye detection are being used for analyzing driver"s behaviour with the head pose detection [9], in designing simulators for surgical and medical applications [10], for capturing and controlling brain signals as a Brain Computer Interface (BCI) [11]. HCI techniques are also being used to build gaze control strategy for controlling different devices like microscopes with eye, GUI based eye gaze application control, estimating user"s interaction in the simulated environment and developing simulator for computer games etc.…”

Section: Introductionmentioning

confidence: 99%

“…The main functions of eye gazing systems are the human activity recognition, facial expression recognition, pointing and selection, activating commands and combinations with other pointing devices. Similarly the outcomes from the eye trackers or models can also be used for capturing and controlling brain signals as a BCI [11], eye gaze interfaces using EOG [14], gaze tracking system for children [15]. Eye gaze based techniques can also be used to increase security or authenticity of input codes for financial transactions etc.…”

Section: Introductionmentioning

confidence: 99%

Eye Gaze Techniques for Human Computer Interaction: A Research Survey

Sharma¹,

Abrol²

2013

IJCA

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Human Computer Interaction (HCI) is an emerging technology. Eye gaze technique is one of the very significant techniques of HCI and can be used as hands free pointing tool enabling hands-free operation of the display for the user. The important advantage in using eye gaze systems is that the user can communicate from a distance, and there is no requirement of physical contact with the computer. Investigation of eye gaze helps to understand various aspects of the user like attention, intention, desire and area of interest etc. The eye gaze detection techniques can be classified on the basis of direct eye detection, appearance, template, shape, feature, motion, hybrid, regression, 3D methods etc. There are significant factors like shape and size of the object, distance from the subject, texture, light conditions, colour, orientation, head movement, calibration which may influence and affect the efficiency and effectiveness of the eye gaze detection. The use of the gaze as a human computer interface in different fields is an example of high end applications of these techniques. Eye detection is being used in many real time and interactive high end applications. These include the tracking and analyzing of driver"s behaviour with the head pose detection. It is being used for assessing consumer"s shopping behaviour, pointing and selection, activating commands and combinations with other pointing devices, in surgical and medical applications. Moreover eye gaze techniques are also useful for designing and development of various devices especially for differently abled users.In this paper an extensive research survey has been carried out to understand and analyze the study of various eye gaze techniques, algorithms and models. On the basis of survey of various techniques of eye gaze, a general overview of different phases of eye gaze processing has been presented. Certain technical factors have been identified that are significant and relevant for the working of the models. In the literature survey a number of parameters that are significant for estimation, detection, better efficiency and accuracy of eye gaze techniques have been studied and analysed. A comparison and analytical discussion of different eye gaze techniques and models have been presented. The analysis and classification of the models shall be helpful for further improvement and optimization in the performance and accuracy of eye gaze techniques. General TermHuman computer interaction. KeywordsEye gaze techniques and models, feature based classification and comparison, phases, gaze detection and estimation.

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Development of a Low-Cost FPGA-Based SSVEP BCI Multimedia Control System

Cited by 80 publications

References 23 publications

An Efficient Hardware Implementation for a Motor Imagery Brain Computer Interface System

An Efficient Hardware Implementation for a Motor Imagery Brain Computer Interface System

Design and Implementation of a Novel Complete Filter for EEG Application on FPGA

Eye Gaze Techniques for Human Computer Interaction: A Research Survey

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