Closed Loop Subject-Independent SSVEP Frequency Detection System Using CCA Features and Ensemble Learning Methods

Brain Computer Interfacing (BCI) systems, which are a new communicating channel between humans and the computers are growing rapidly. One such a method is based on the Steady State Visual Evoked Potentials (SSVEP), which can be recorded during visual stimulating of the subject by a twinkling light source with a fixed frequency. An important parameter to be considered is the effect of the inter-sources distance on the accuracy of such BCI systems. In particular inter-sources (LEDs) distances of 4, 14, 24, 44 and 64 cm when the sources plane is located 60 cm away from the subject's eyes (producing inter-sources visual angles of 3.8°, 13.4°, 22.6°, 40.2° and 56° respectively) were examined. In addition, four different sweep lengths of 0.5, 1, 2 and 3 seconds are considered. In addition, due to the usage of the AR models for feature extraction from the SSVEP signals, selection of the best AR model together with the best classifier among the LDA, the SVM and the Naive Bayes are studied. It is showed that the BCI system with D=44 cm, AR order of 13 and either the LDA or the SVM classifiers could produce the best results compared to the other cases.

A classification algorithm of an SSVEP brain-Computer interface based on CCA fusion wavelet coefficients

Brain-computer interface (BCI) systems have emerged as an augmentative technology that can provide a promising solution for individuals with motor dysfunctions and for the elderly who are experiencing muscle weakness. Steady-state visually evoked potentials (SSVEPs) are widely adopted in BCI systems due to their high speed and accuracy when compared to other BCI paradigms. In this paper, we apply combined magnitude and phase features for class discrimination in a real-time SSVEP-based BCI platform. In the proposed real-time system users gain control of a motorised bed system with seven motion commands and an idle state. Experimental results amongst eight participants demonstrate that the proposed real-time BCI system can successfully discriminate between different SSVEP signals achieving high information transfer rates (ITR) of 82.73 bits/min. The attractive features of the proposed system include noninvasive recording, simple electrode configuration, excellent BCI response and minimal training requirements.

Visual evoked potential (VEP)-related EEG signals have attracted widespread attention in the construction of brain-computer interface (BCI) systems. However, long-term use of the BCI system is prone to fatigue. Effectively coping with fatigue is the key to expanding BCI application scenarios. In this brief, we conduct two VEP experiments and propose a novel, concise and practical convolutional neural network (CNN) model to study and solve this problem. In detail, firstly, in terms of the stimulus frequencies in VEP experiments, we intercept the spectrum sequences of the fundamental waves and two harmonics. All these spectrum sequences are then paralleled input into the proposed multi-harmonic linkage CNN (MHLCNN) model, through three branches. Finally, the linkage characteristics of multiple harmonics are combined for VEP signal classification. The experimental results demonstrate that the proposed model shows good scalability, and can respectively improve the average classification accuracy by 23.17% and 15.85% when applied to steady-state VEP (SSVEP) signals in fatigue state and steady-state motion VEP (SSMVEP) signals, compared with the benchmark methods. All these provide a feasible and high-quality solution for reducing the impact of fatigue on the performance of VEP-based BCI systems.

Brain computer interface (BCI) technology is a communication and control approach. Up to now many studies have attempted to develop an EEG-based BCI system to improve the quality of life of people with severe disabilities, such as amyotrophic lateral sclerosis (ALS), paralysis, brain stroke and so on. The proposed BCIBSHS could help to provide a new way for supporting life of paralyzed people and elderly people. The goal of this paper is to explore how to set up a cost-effective and safe-to-use online BCIBSHS to recognize multi-commands and control smart devices based on SSVEP. The portable EEG acquisition device (Emotiv EPOC) was used to collect EEG signals. The raw signals were denoised by discrete wavelet transform (DWT) method, and then the canonical correlation analysis (CCA) method was used for feature extraction and classification. Another part is the control of smart home devices. The classification results of SSVEP can be translated into commands to control several devices for the smart home. Here, the Power over Ethernet (PoE) technology was utilized to provide electrical energy and communication for those devices. During online experiments, four different control commands have been achieved to control four smart home devices (lamp, web camera, guardianship telephone and intelligent blinds). Experimental results showed that the online BCIBSHS obtained 86.88 ± 5.30% average classification accuracy rate. The BCI and PoE technology, combined with smart home system, overcoming the shortcomings of traditional systems and achieving home applications management rely on EEG signal. In this paper, we proposed an online steady-state visual evoked potential (SSVEP) based BCI system on controlling several smart home devices.

One of the main problems in Brain Computer Interface (BCI) systems is the non-stationary behavior of the electroencephalography (EEG) signals causing problems in real time applications. Another common problem in BCI systems is the situation where the labeled data are scarce. In this study, we take a semi-supervised learning perspective and propose solving both types of problems by updating the BCI system with labels obtained from the outputs of the classifier. To test the approach, data from motor imagery BCI system are used. Attributes extracted from EEG signals are classified with Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). With respect to the static classifiers, accuracy was improved approximately 4% using the proposed adaptation approach in the case of a training dataset. Even though the difference between the performance of static and adaptive classifiers decreases as the size of training data increases, the accuracy of our proposed adaptive classifier remains higher. The proposed approach has also improved the performance of a BCI system around 4% in the case of non-stationary signals as well.

Brain-Computer Interfacing (BCI) systems can be used to improve the quality of life of disabled individuals. Electroencephalography (EEG) Steady-State Visual Evoked Potentials (SSVEP) based BCI systems provide a non-invasive, inexpensive method of communication and control with minimal user training. Among many applications of BCI systems, robot control is widely used to restore motor functions of individuals with severe neuromuscular disabilities. In this paper, a low-cost, single-channel, SSVEP based BCI system is implemented to control the motion of the wrist and the gripper of a robot arm. The SSVEP user commands are generated by focusing the user's gaze on a set of light-emitting diodes (LED) flickering at different frequencies. For the identification of user intent to generate control commands, a classification algorithm is proposed, which is based on Euclidean distance measurement between prominent peaks of the SSVEP Fast Fourier Transform (FFT) spectrum, and the fundamental and harmonic spectral content of stimulus frequency.

Steady-state visual evoked potential (SSVEP) has been widely applied in brain computer interface (BCI) systems. The amplitude and phase features of SSVEP were commonly extracted by Fourier analysis method from single-channel EEG data. In the multichannel case, canonical correlation analysis (CCA) has been utilized for the analysis of frequency coding SSVEP. This paper presents the analysis of phase coding SSVEP using CCA. The phase coding scheme consists of six targets flickering at 10Hz, with a 60° phase difference between any two sequential targets. For each target, 20 trials of 8s EEG signal were acquired. Using CCA, we achieve channel selection and extraction of phase features; a classification accuracy of above 80% is obtained, with the length of the time window up to 4s. The results demonstrate that phase coding SSVEP analysis based on CCA is feasible.

Brain-computer interface (BCI) systems translate the human neurophysiological activities into commands through EEG analysis. Improving the BCI performances leads to faster and easier use and less fatigue. In this study, we proposed a new prepossessing approach to increase the robustness of a steady-state visual evoked potential (SSVEP) based BCI. Inspiring from the known properties of the SSVEP frequency components, the goal was to enhance the signal quality by making it more convenient to be interpreted by the decision-making step. We first investigated the potential to detect the deteriorating periods based on the physiological properties of the SSVEP. The proposed system localizes the intervals which can obscure the SSVEP frequencies by a new algorithm founded on the processing and the analysis of the instantaneous phase. The piecewise linear regression allows a sampler comprehension of the phase signal. Then, these intervals are filtered by the moving average filter to enhance the SSVEP quality. Finally, the decision making is made by the canonical correlation analysis (CCA) algorithm. The results of experiments, using real EEG signals from five subjects, show that the proposed approach significantly increases the performances in terms of accuracy and information transfer rate by about 7.3% and 3.85 bits/min, respectively, in case of 2 s segment length. On the other hand, the spatial filtering methods of the literature weaken the system performances.

Objective. Brain–computer interface (BCI) technologies have been intensely studied to provide alternative communication tools entirely independent of neuromuscular activities. Current BCI technologies use electroencephalogram (EEG) acquisition methods that require unpleasant gel injections, impractical preparations and clean-up procedures. The next generation of BCI technologies requires practical, user-friendly, nonintrusive EEG platforms in order to facilitate the application of laboratory work in real-world settings. Approach. A capacitive electrode that does not require an electrolytic gel or direct electrode–scalp contact is a potential alternative to the conventional wet electrode in future BCI systems. We have proposed a new capacitive EEG electrode that contains a conductive polymer-sensing surface, which enhances electrode performance. This paper presents results from five subjects who exhibited visual or auditory steady-state responses according to BCI using these new capacitive electrodes. The steady-state visual evoked potential (SSVEP) spelling system and the auditory steady-state response (ASSR) binary decision system were employed. Main results. Offline tests demonstrated BCI performance high enough to be used in a BCI system (accuracy: 95.2%, ITR: 19.91 bpm for SSVEP BCI (6 s), accuracy: 82.6%, ITR: 1.48 bpm for ASSR BCI (14 s)) with the analysis time being slightly longer than that when wet electrodes were employed with the same BCI system (accuracy: 91.2%, ITR: 25.79 bpm for SSVEP BCI (4 s), accuracy: 81.3%, ITR: 1.57 bpm for ASSR BCI (12 s)). Subjects performed online BCI under the SSVEP paradigm in copy spelling mode and under the ASSR paradigm in selective attention mode with a mean information transfer rate (ITR) of 17.78 ± 2.08 and 0.7 ± 0.24 bpm, respectively. Significance. The results of these experiments demonstrate the feasibility of using our capacitive EEG electrode in BCI systems. This capacitive electrode may become a flexible and non-intrusive tool fit for various applications in the next generation of BCI technologies.

Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems suffer from low SSVEP response intensity and visual fatigue, resulting in lower accuracy when operating the system for continuous commands, such as an electric wheelchair control. This study proposes two SSVEP improvements to create a practical BCI for communication and control in disabled people. The first is flicker pattern modification for increasing SSVEP response through mixing (1) fundamental and first harmonic frequencies, and (2) two fundamental frequencies for an additional number of commands. The second method utilizes a quick response (QR) code for visual stimulus patterns to increase the SSVEP response and reduce visual fatigue. Eight different stimulus patterns from three flickering frequencies (7, 13, and 17 Hz) were presented to twelve participants for the test and score levels of visual fatigue. Two popular SSVEP methods, i.e., power spectral density (PSD) with Welch periodogram and canonical correlation analysis (CCA) with overlapping sliding window, are used to detect SSVEP intensity and response, compared to the checkerboard pattern. The results suggest that the QR code patterns can yield higher accuracy than checkerboard patterns for both PSD and CCA methods. Moreover, a QR code pattern with low frequency can reduce visual fatigue; however, visual fatigue can be easily affected by high flickering frequency. The findings can be used in the future to implement a real-time, SSVEP-based BCI for verifying user and system performance in actual environments.

Application of a reconstruction technique in detection of dominant SSVEP frequency

Brain Computer Interfaces (BCI) rely on brain waves signal, such as electro-encephalogram (EEG) recording, to endow a disabled user with non-muscular communication. Given the very low signal-to-noise ratio of EEG, a signal enhancement phase is crucial for ensuring decent performances in BCI systems. Several methods have been proposed for EEG signal enhancement, such as Independent Component Analysis, Common Spatial Pattern, and Principal Component Analysis. We show that Canonical Correlation Analysis (CCA), initially introduced to SSVEP-based BCI as a feature extraction method, is a good candidate for such preprocessing state. Evaluation is performed on a recording from 5 subjects during a BCI task based on Steady-State Visual Evoked Potentials (SSVEP). The authors demonstrate that CCA significantly improves classification performances in SSVEP-based BCIs.

Spectrum-Enhanced TRCA (SE-TRCA): A novel approach for direction detection in SSVEP-based BCI

The structure and functioning of the human brain has always attracted the attention of scientists. In the past 10-20 years, one of the popular areas of study related to the human brain is the Brain Computer Interface (BCI) systems. Different brain monitoring techniques such as EEG, fNIRS, MEG can be used to observe human brain activities in BCI systems. In addition to the differences in monitoring methods in BCI systems, the focus of system operation may also change. In some BCI systems, it may be preferable to focus on Motor Imagery in another system, while the focus is on the P300 signals in EEG data. This requires a differentiation between the systems during the operational phases of the BCI system. One of the most significant points of system differentiation is the classification stage of the BCI system. In this study, a compilation of classification methods commonly used in Brain Computer Interface systems based on EEG based Motor Imagery will be carried out. This study will provide researchers with information about the methods that can be used in their own systems for classification in Brain Computer Interface systems based on EEG based Motor Imagery.