A gaze independent brain-computer interface based on visual stimulation through closed eyelids

A classical brain-computer interface (BCI) based on visual event-related potentials (ERPs) is of limited application value for paralyzed patients with severe oculomotor impairments. In this study, we introduce a novel gaze independent BCI paradigm that can be potentially used for such end-users because visual stimuli are administered on closed eyelids. The paradigm involved verbally presented questions with 3 possible answers. Online BCI experiments were conducted with twelve healthy subjects, where they selected one option by attending to one of three different visual stimuli. It was confirmed that typical cognitive ERPs can be evidently modulated by the attention of a target stimulus in eyes-closed and gaze independent condition and further classified with high accuracy during online operation (74.58% ± 17.85 s.d.; chance level 33.33%), demonstrating the effectiveness of the proposed novel visual ERP paradigm. Also, stimulus-specific eye movements observed during stimulation were verified as reflex responses to light stimuli and they did not contribute to classification. To the best of our knowledge, this study is the first to show the possibility of using a gaze independent visual ERP paradigm in an eyes-closed condition, thereby providing another communication option for severely locked-in patients suffering from complex ocular dysfunctions.BMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine InteraktionBMBF, 16SV5839, Maschinelles Lernen zur Optimierung der Kommunikation schwerstgelähmter Patienten per BC

Non-invasive EEG-based BCI spellers from the beginning to today: a mini-review

The defeat of the central motor neuron leads to the motor disorders. Patients lose the ability to control voluntary muscles, for example, of the upper limbs, which introduces a fundamental dissonance in the possibility of daily use of a computer or smartphone. As a result, the patients lose the ability to communicate with other people. The article presents the most popular paradigms used in the brain-computer-interface speller system and designed for typing by people with severe forms of the movement disorders. Brain-computer interfaces (BCIs) have emerged as a promising technology for individuals with communication impairments. BCI-spellers are systems that enable users to spell words by selecting letters on a computer screen using their brain activity. There are three main types of BCI-spellers: P300, motor imagery (MI), and steady-state visual evoked potential (SSVEP). However, each type has its own limitations, which has led to the development of hybrid BCI-spellers that combine the strengths of multiple types. Hybrid BCI-spellers can improve accuracy and reduce the training period required for users to become proficient. Overall, hybrid BCI-spellers have the potential to improve communication for individuals with impairments by combining the strengths of multiple types of BCI-spellers. In conclusion, BCI-spellers are a promising technology for individuals with communication impairments. P300, MI, and SSVEP are the three main types of BCI-spellers, each with their own advantages and limitations. Further research is needed to improve the accuracy and usability of BCI-spellers and to explore their potential applications in other areas such as gaming and virtual reality

Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems

© 2017 The Author(s). Background: One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. Methods: This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. Results: Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s

Optimizing the face paradigm of BCI system by modified mismatch negative paradigm

Many recent studies have focused on improving the performance of event-related potential (ERP) based brain computer interfaces (BCIs). The use of a face pattern has been shown to obtain high classification accuracies and information transfer rates (ITRs) by evoking discriminative ERPs (N200 and N400) in addition to P300 potentials. Recently, it has been proved that the performance of traditional P300-based BCIs could be improved through a modification of the mismatch pattern. In this paper, a mismatch inverted face pattern (MIF-pattern) was presented to improve the performance of the inverted face pattern (IF-pattern), one of the state of the art patterns used in visual-based BCI systems. Ten subjects attended in this experiment. The result showed that the mismatch inverted face pattern could evoke significantly larger vertex positive potentials (p < 0.05) and N400s (p < 0.05) compared to the inverted face pattern. The classification accuracy (mean accuracy is 99.58%) and ITRs (mean bit rate is 27.88 bit/min) of the mismatch inverted face pattern was significantly higher than that of the inverted face pattern (p < 0.05)

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