Assessing the quality of steady-state visual-evoked potentials for moving humans using a mobile electroencephalogram headset.

Recent advances in mobile electroencephalogram (EEG) systems, featuring non-prep dry electrodes and wireless telemetry, have enabled and promoted the applications of mobile brain-computer interfaces (BCIs) in our daily life. Since the brain may behave differently while people are actively situated in ecologically-valid environments versus highly-controlled laboratory environments, it remains unclear how well the current laboratory-oriented BCI demonstrations can be translated into operational BCIs for users with naturalistic movements. Understanding inherent links between natural human behaviors and brain activities is the key to ensuring the applicability and stability of mobile BCIs. This study aims to assess the quality of steady-state visual-evoked potentials (SSVEPs), which is one of promising channels for functioning BCI systems, recorded using a mobile EEG system under challenging recording conditions, e.g., walking. To systematically explore the effects of walking locomotion on the SSVEPs, this study instructed subjects to stand or walk on a treadmill running at speeds of 1, 2, and 3 mile (s) per hour (MPH) while concurrently perceiving visual flickers (11 and 12 Hz). Empirical results of this study showed that the SSVEP amplitude tended to deteriorate when subjects switched from standing to walking. Such SSVEP suppression could be attributed to the walking locomotion, leading to distinctly deteriorated SSVEP detectability from standing (84.87 ± 13.55%) to walking (1 MPH: 83.03 ± 13.24%, 2 MPH: 79.47 ± 13.53%, and 3 MPH: 75.26 ± 17.89%). These findings not only demonstrated the applicability and limitations of SSVEPs recorded from freely behaving humans in realistic environments, but also provide useful methods and techniques for boosting the translation of the BCI technology from laboratory demonstrations to practical applications

Steady-State movement related potentials for brain–computer interfacing

An approach for brain-computer interfacing (BCI) by analysis of steady-state movement related potentials (ssMRPs) produced during rhythmic finger movements is proposed in this paper. The neurological background of ssMRPs is briefly reviewed. Averaged ssMRPs represent the development of a lateralized rhythmic potential, and the energy of the EEG signals at the finger tapping frequency can be used for single-trial ssMRP classification. The proposed ssMRP-based BCI approach is tested using the classic Fisher's linear discriminant classifier. Moreover, the influence of the current source density transform on the performance of BCI system is investigated. The averaged correct classification rates (CCRs) as well as averaged information transfer rates (ITRs) for different sliding time windows are reported. Reliable single-trial classification rates of 88%-100% accuracy are achievable at relatively high ITRs. Furthermore, we have been able to achieve CCRs of up to 93% in classification of the ssMRPs recorded during imagined rhythmic finger movements. The merit of this approach is in the application of rhythmic cues for BCI, the relatively simple recording setup, and straightforward computations that make the real-time implementations plausible

Classification of Frequency and Phase Encoded Steady State Visual Evoked Potentials for Brain Computer Interface Speller Applications using Convolutional Neural Networks

Over the past decade there have been substantial improvements in vision based Brain-Computer Interface (BCI) spellers for quadriplegic patient populations. This thesis contains a review of the numerous bio-signals available to BCI researchers, as well as a brief chronology of foremost decoding methodologies used to date. Recent advances in classification accuracy and information transfer rate can be primarily attributed to time consuming patient specific parameter optimization procedures. The aim of the current study was to develop analysis software with potential ‘plug-in-and-play’ functionality. To this end, convolutional neural networks, presently established as state of the art analytical techniques for image processing, were utilized. The thesis herein defines deep convolutional neural network architecture for the offline classification of phase and frequency encoded SSVEP bio-signals. Networks were trained using an extensive 35 participant open source Electroencephalographic (EEG) benchmark dataset (Department of Bio-medical Engineering, Tsinghua University, Beijing). Average classification accuracies of 82.24% and information transfer rates of 22.22 bpm were achieved on a BCI naïve participant dataset for a 40 target alphanumeric display, in absence of any patient specific parameter optimization

Electroencephalogram Signal Processing For Hybrid Brain Computer Interface Systems

The goal of this research was to evaluate and compare three types of brain computer interface (BCI) systems, P300, steady state visually evoked potentials (SSVEP) and Hybrid as virtual spelling paradigms. Hybrid BCI is an innovative approach to combine the P300 and SSVEP. However, it is challenging to process the resulting hybrid signals to extract both information simultaneously and effectively. The major step executed toward the advancement to modern BCI system was to move the BCI techniques from traditional LED system to electronic LCD monitor. Such a transition allows not only to develop the graphics of interest but also to generate objects flickering at different frequencies. There were pilot experiments performed for designing and tuning the parameters of the spelling paradigms including peak detection for different range of frequencies of SSVEP BCI, placement of objects on LCD monitor, design of the spelling keyboard, and window time for the SSVEP peak detection processing. All the experiments were devised to evaluate the performance in terms of the spelling accuracy, region error, and adjacency error among all of the paradigms: P300, SSVEP and Hybrid. Due to the different nature of P300 and SSVEP, designing a hybrid P300-SSVEP signal processing scheme demands significant amount of research work in this area. Eventually, two critical questions in hybrid BCl are: (1) which signal processing strategy can best measure the user\u27s intent and (2) what a suitable paradigm is to fuse these two techniques in a simple but effective way. In order to answer these questions, this project focused mainly on developing signal processing and classification technique for hybrid BCI. Hybrid BCI was implemented by extracting the specific information from brain signals, selecting optimum features which contain maximum discrimination information about the speller characters of our interest and by efficiently classifying the hybrid signals. The designed spellers were developed with the aim to improve quality of life of patients with disability by utilizing visually controlled BCI paradigms. The paradigms consist of electrodes to record electroencephalogram signal (EEG) during stimulation, a software to analyze the collected data, and a computing device where the subject’s EEG is the input to estimate the spelled character. Signal processing phase included preliminary tasks as preprocessing, feature extraction, and feature selection. Captured EEG data are usually a superposition of the signals of interest with other unwanted signals from muscles, and from non-biological artifacts. The accuracy of each trial and average accuracy for subjects were computed. Overall, the average accuracy of the P300 and SSVEP spelling paradigm was 84% and 68.5 %. P300 spelling paradigms have better accuracy than both the SSVEP and hybrid paradigm. Hybrid paradigm has the average accuracy of 79 %. However, hybrid system is faster in time and more soothing to look than other paradigms. This work is significant because it has great potential for improving the BCI research in design and application of clinically suitable speller paradigm

Stimuli design for SSVEP-based brain computer-interface

The paper presents a process of stimuli designfor SSVEP-based brain computer-interface. A brain computerinterfacecan be used in direct communication between a brainand a computer, without using muscles. This device is useful forparalyzed people to communicate with the surrounding environment.Design process should provide high accuracy recognitionof presented stimuli and high user comfort. It is widely knownhow to make stimuli for BCI which are using high-grade EEG.Over recent years cheaper EEGs are becoming more and morepopular, for example OpenBCI, which uses ADS1299 amplifier.In this article we review past works of other authors and compareit with our results, obtained using EEG mentioned before. Wetry to confirm that it is possible to use successfully OpenBCI inBCI projects

Dual-Frequency SSVEP-based BCI for Reducing Eye Fatigue and Improving Classification Rate

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2016. 2. 박광석.The steady-state visual-evoked potential (SSVEP)-based brain-computer interface (BCI) has been widely investigated because of its high signal-to-noise ratio (SNR), and little requirement for training. However, the stimulus for evoking SSVEP causes high visual fatigue and has a risk of epileptic seizure. Furthermore, stimulation frequency is limited and the SSVEP amplitude diminishes when a monitor is used as a stimulator. In this thesis, a dual-frequency SSVEP is examined to resolve the aforementioned issues. Employing dual-frequency SSVEPs, two novel SSVEP-based BCIs are introduced to decrease eye fatigue and use harmonic frequencies with increased performance. First, the spectral characteristics of dual-frequency SSVEPs are investigated and frequency recognition methods for dual-frequency SSVEPs are suggested. Three methods based on power spectral density analysis (PSDA) and two methods based on canonical correlation analysis (CCA) were tested. The proposed CCA with a novel reference signal exhibited the best BCI performance, and the use of harmonic components improved the classification rate of the dual-frequency SSVEP. Second, the dual-frequency SSVEP response to an amplitude-modulated stimulus (AM-SSVEP) was explored to verify its performance with reduced eye fatigue. An amplitude-modulated stimulus was generated using the product of two sine waves at a carrier frequency (fc) and a modulating frequency (fm). The carrier frequency was higher than 40 Hz to reduce eye fatigue, and the modulating frequency ranged around the α-band (9–12 Hz) to utilize low-frequency harmonic information. The feasibility of AM-SSVEP with high BCI performance and low eye fatigue was confirmed through offline and online experiments. Using an optimized combination of the harmonic frequencies, the online experiments demonstrated that the accuracy of the AM-SSVEP was 97%, equivalent to that of the low-frequency SSVEP. Furthermore, subject evaluation indicated that an AM stimulus caused lower eye fatigue and less perception of flickering than a low-frequency stimulus, in a manner similar to a high-frequency stimulus. Third, a novel dual-frequency SSVEP-based hybrid SSVEP-P300 speller is introduced to overcome the frequency limitations and improve the performance. The hybrid speller consists of nine panels flickering at different frequencies. Each panel contains four different characters that appear in a random sequence. The flickering panel and the periodically updating character evoke the dual-frequency SSVEP, and the oddball stimulus of the target character evokes the P300. Ten subjects participated in offline and online experiments, in which accuracy and information transfer rate (ITR) were compared with those of conventional SSVEP and P300 spellers. The offline analysis revealed that the proposed speller elicited dual-frequency SSVEP. Moreover, the dual-frequency SSVEP significantly improved the SSVEP classification rate and ITR with a monitor in online experiments by 4 % accuracy and 18.8 bpm ITR. In conclusion, the proposed dual-frequency SSVEP-based BCIs reduce eye fatigue and improve SSVEP classification rate. The results indicate that this study provides a promising approach to make SSVEP-based BCIs more reliable and efficient for practical use.1. Introduction 1 1.1. Brain-Computer Interface 1 1.1.1. Basic Concepts 1 1.1.2. SSVEP-based BCIs 2 1.1.3. P300-based BCIs 5 1.1.4. Hybrid SSVEP-P300 BCIs 6 1.2. Motivation and Objectives 7 2. Frequency Recognition Methods for DFSSVEP-based BCI 11 2.1. Basic Concepts 11 2.2. DFSSVEP Recognition Methods 16 2.2.1. PSDA-based Methods 17 2.2.2. CCA-based Methods 20 2.3. Offline Analysis 23 2.3.1. Dual-Frequency Stimulus 23 2.3.2. Experimental Settings 24 2.3.3. Spectral Analysis of DFSSVEP 25 2.3.4. Signal Processing 26 2.4. Results 27 2.4.1. Harmonic Frequency 27 2.4.2. Comparison of Recognition Rates 28 2.5. Conclusion 31 3. DFSSVEP-based BCI for Reducing Eye Fatigue 33 3.1. Basic Concepts 33 3.1.1. Amplitude Modulation Technique 33 3.1.2. Amplitude-Modulated Stimuli for Evoking AM-SSVEP 35 3.2. Methods 38 3.2.1. Subjects and Experimental Settings 38 3.2.2. Offline Experiments 41 3.2.3. EEG Analysis 43 3.2.4. Online Experiments 45 3.3. Results 50 3.3.1. Harmonics of AM-SSVEP 50 3.3.2. Offline Analysis 54 3.3.3. CFC for Online Analysis 57 3.3.4. Online Analysis 59 3.3.5. Subject Evaluation 64 3.4. Discussion 66 3.4.1. Combining of Low- and High-Frequency SSVEPs 66 3.4.2. AM Harmonic Frequencies in CFC 70 3.4.3. Error Analysis 71 3.4.4. Effects of Environmental Illumination 74 3.5. Conclusion 76 4. DFSSVEP-based Hybrid BCI for Improving Classification Rate 79 4.1. Basic Concepts 79 4.2. Methods 85 4.2.1. Experimental Setting 85 4.2.2. Experimental Procedure 88 4.2.3. Signal Processing 89 4.2.4. Statistical Comparison of the EEG Responses 91 4.2.5. BCI Performance 92 4.3. Results 94 4.3.1. EEG Response to the Hybrid Speller 94 4.3.2. Offline Analysis 99 4.3.3. Online Analysis 102 4.4. Discussion 104 4.4.1. DFSSVEP 104 4.4.2. ITR Comparison with Conventional Spellers 109 4.4.3. ITR Comparison with Previous Studies 110 4.4.4. ITR with Different Visual Angle 114 4.4.5. Limitations 117 4.5. Conclusion 118 5. Conclusion 119 6. References 123 국문 초록 133Docto