Decoding steady-state visual evoked potentials from electrocorticography

We report on a unique electrocorticography (ECoG) experiment in which Steady-State Visual Evoked Potentials (SSVEPs) to frequency-and phase-tagged stimuli were recorded from a large subdural grid covering the entire right occipital cortex of a human subject. The paradigm is popular in EEG-based Brain Computer Interfacing where selectable targets are encoded by different frequency-and/or phase-tagged stimuli. We compare the performance of two state-of-the-art SSVEP decoders on both ECoG-and scalp-recorded EEG signals, and show that ECoG-based decoding is more accurate for very short stimulation lengths (i.e., less than 1 s). Furthermore, whereas the accuracy of scalp-EEG decoding bene fi ts from a multi-electrode approach, to address interfering EEG responses and noise, ECoG decoding enjoys only a marginal improvement as even a single electrode, placed over the posterior part of the primary visual cortex, seems to suf fi ce. This study shows, for the fi rst time, that EEG-based SSVEP decoders can in principle be applied to ECoG, and can be expected to yield faster decoding speeds using less electrodes

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

Enhancement of SSVEPs Classification in BCI-based Wearable Instrumentation Through Machine Learning Techniques

This work addresses the adoption of Machine Learning classifiers and Convolutional Neural Networks to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces. The proposed measurement system is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In particular, Head-Mounted Displays for Augmented Reality are used to generate and display the flickering stimuli for the SSVEPs elicitation. Four experiments were conducted by employing, in turn, a different Head-Mounted Display. For each experiment, two different algorithms were applied and compared with the state-of-the-art-techniques. Furthermore, the impact of different Augmented Reality technologies in the elicitation and classification of SSVEPs was also explored. The experimental metrological characterization demonstrates (i) that the proposed Machine Learning-based processing strategies provide a significant enhancement of the SSVEP classification accuracy with respect to the state of the art, and (ii) that choosing an adequate Head-Mounted Display is crucial to obtain acceptable performance. Finally, it is also shown that the adoption of inter-subjective validation strategies such as the Leave-One-Subject-Out Cross Validation successfully leads to an increase in the inter-individual 1-σ reproducibility: this, in turn, anticipates an easier development of ready-to-use systems

Chapter BCI Integration: Application Interfaces

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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