Query: CCA SSVEP

title: Efficient representations of EEG signals for SSVEP frequency recognition based on deep multiset CCA
abstract: Canonical correlation analysis (CCA) has been widely used for frequency recognition in steady state visual evoked potential (SSVEP) based brain computer interfaces (BCIs) However, linear CCA based methods may be insufficient given the complexity of EEG signals. A nonlinear feature extraction method based on deep multiset CCA (DMCCA) is proposed for SSVEP recognition to fully utilize the real EEG and constructed sine cosine signals. In DMCCA, neural networks are trained to learn the nonlinear representations of multiple sets of EEG signals at the same frequency by maximizing the overall correlation within the representations and reference signals. Therefore, reference signals are augmented with the extracted features for frequency recognition. Finally, the proposed method is evaluated using SSVEP signals collected from 10 subjects. DMCCA based method outperforms others in terms of classification accuracy compared with CCA and multiset CCA based methods. The proposed DMCCA based method has substantial potential for improving the recognition performance of SSVEP signals.

title: Approach to recognizing SSVEP by combining CCA with artificial neural network
abstract: Steady state visual evoked potential (SSVEP) is one of popular EEG patterns employed by brain computer interface (BCI) systems. SSVEP based BCI systems have some advantages such as high information transfer rate and less training requirement for the users. To develop a SSVEP based BCI system, the recognition of the primary frequency of SSVEP is critical and may be effectively achieved by using canonical correlation analysis (CCA) Several improved methods based on the traditional CCA method have been proposed. However, very few researches have considered the effectiveness, both by the individual subject itself and by the diversity of feeling imposed on different subjects by the same stimulus frequency, on the result of CCA. To address this issue, a novel method, NNWCCA, is proposed in this paper. NNWCCA first uses standard CCA to calculate the maximum canonical correlation coefficients between the analyzed EEG data with the reference signal of each stimulus frequency, and then employs an artificial neural network, which takes the calculated canonical correlation coefficients as inputs, to finally determine the primary frequency of the SSVEP hidden in the analyzed EEG data. The effectiveness of NNWCCA was verified by using a public SSVEP data.

title: A Custom Hardware CCA Engine for Real time SSVEP based BCI Applications
abstract: Canonical Correlation Analysis is widely exploited in different signal processing applications such as detection of the brain internal activity in an SSVEP based Brain Computer Interface system. The main disadvantage of the CCA algorithm is its computationally intensive nature which demands numerous complex matrix transformations including inversion, eigenvalue and eigenvector extraction, multiplication, and decomposition. These complexities prevent its efficient hardware implementation while its software implementation also is not so fast to be exploited in real time applications. In this paper, some simplifications are applied to an 8 channel CCA algorithm to make its hardware implementation feasible. The effects of these simplifications on the output accuracy of the algorithm are also demonstrated in the paper. A custom fully hardware fixed point CCA engine is then developed based on the simplified algorithm to achieve higher performance and less power consumption. The FPGA synthesis results of the design are also included in the paper.

title: Decision Making Selector (DMS) for Integrating CCA Based Methods to Improve Performance of SSVEP Based BCIs
abstract: Recent research has demonstrated improved performance of a brain computer interface (BCI) using fusion based approaches. This paper proposes a novel decision making selector (DMS) to integrate classification decisions of different frequency recognition methods based on canonical correlation analysis (CCA) which were used in decoding steady state visual evoked potentials (SSVEPs)  The DMS method selects a decision more likely to be correct from two methods namely as M1 and M2 by separating the M1 false and M2 false trials. To measure the uncertainty of each decision, feature vectors were extracted using the largest and second largest correlation coefficients corresponding to all the stimulus frequencies. The proposed method was evaluated by integrating all pairs of 7 CCA based algorithms, including CCA, individual template based CCA (ITCCA) multi set CCA (MsetCCA) L1 regularized multi way CCA (L1 MCCA) filter bank CCA (FBCCA) extended CCA (ECCA) and task related component analysis (TRCA)  The experimental results obtained from a 40 target dataset of thirty five subjects showed that the proposed DMS method was validated to obtain an enhanced performance by integrating the algorithms with close accuracies.  The results suggest that the proposed DMS method is effective in integrating decisions of different methods to improve the performance of SSVEP based BCIs.

title: Channel Projection Based CCA Target Identification Method for an SSVEP Based BCI System of Quadrotor Helicopter Control
abstract: The brain computer interface (BCI) plays an important role in assisting patients with amyotrophic lateral sclerosis (ALS) to enable them to participate in communication and entertainment. In this study, a novel channel projection based canonical correlation analysis (CP CCA) target identification method for steady state visual evoked potential (SSVEP based BCI system was proposed. The single channel electroencephalography (EEG) signals of multiple trials were recorded when the subject is under the same stimulus frequency. The CCAs between single channel EEG signals of multiple trials and sine cosine reference signals were obtained. Then, the optimal reference signal of each channel was utilized to estimate the test EEG signal. To validate the proposed method, we acquired the training dataset with two testing conditions including the optimal time window length and the number of the trial of training data. The offline experiments conducted a comparison of the proposed method with the traditional canonical correlation analysis (CCA) and power spectrum density analysis (PSDA) method using a 5 class SSVEP dataset that was recorded from 10 subjects. Based on the training dataset, the online 3D helicopter control experiment was carried out. The offline experimental results showed that the proposed method outperformed the CCA and the PSDA methods in terms of classification accuracy and information transfer rate (ITR) Furthermore, the online experiments of 3 DOF helicopter control achieved an average accuracy of 87.94 5.93% with an ITR of 21.07 4.42 bit/min.

title: An SSVEP Recognition Method by Combining Individual Template with CCA
abstract: Steady state visual evoked potential (SSVEP) based Brain computer interface (BCI) is one of the non invasive BCI systems that has received great attention over the years. It requires shorter training and provides relatively higher information transfer rate. Various approaches have been developed to recognize SSVEP. One popular approach is based on canonical correlation analysis (CCA) The standard CCA uses artificial reference templates of pure sine and cosine signals with frequencies corresponding to the frequencies of the visual stimuli. These reference templates may not optimally reflect the natural SSVEP features hidden in the recorded EEG signal. Thus, they may not produce high performance. This paper introduced a new approach called ITCCA (Individual Template Canonical Correlation Analysis) ITCCA optimized the reference signal by incorporating features of the subject's EEG data into the artificial reference template. The performance of this approach was compared with that of the standard CCA. The experiment showed that ITCCA obtained better results than the standard CCA method.

title: Optimizing SSVEP based brain computer interface with CCA and Genetic Algorithms
abstract: Brain computer interface (BCI) is used for non muscular communication between the human brain and an external device. One of the most commonly utilized phenomena in BCI are steady state visually evoked potentials (SSVEP) that are measured with electroencephalography (EEG) If the subject focuses his/her attention on the stimulus flashing with specified frequency on the computer screen, a signal fluctuations with the same frequency may be observed in his or her visual cortex. Canonical Correlation Analysis (CCA) was adapted to EEG signal analysis and the outcomes of this straightforward approach are more promising in terms of obtaining decoding accuracy than conventional methods. In CCA two sets of data are being compared: a multichannel signal, and a reference prior corresponding to the classes between which the distinction is being made. Category of the signal (e.g. 8 Hz vs 14 Hz) is determined on the basis of the correlation coefficient between the acquired signal and several reference signals. The aim of this paper is to present an idea of utilizing genetic algorithms to generate these reference priors. Outcomes of our analyses suggest that a genetic approach (with signal encoding in the form of the binary code) and tournament selection method may improve the accuracy of SSVEP classification. Further work may help to improve the accuracy even more, with different encoding scheme or alternative and/or more sophisticated selection approach.

title: CCA Model with Training Approach to Improve Recognition Rate of SSVEP in Real Time
abstract: Brain Computer Interfaces (BCIs) are often used to control external devices using electroencephalogram (EEG) signals. In Steady State Visually Evoked Potentials (SSVEP) based BCIs, suboptimal Information Transfer Rate (ITR) is achieved due to false detection of SSVEP as one of the target class while the subject is not focusing on any target. To alleviate this issue, we propose a class labelling method where a classifier is trained against the non target class. In the experiment, features are extracted using Canonical Correlation Analysis (CCA) and class labelling is performed using the proposed method. Afterwards, Linear Discriminant Analysis (LDA) has been employed for classification task. The results were compared with standard methods such as CCA and Fast Fourier Transform (FFT) implemented for the same experimental setup. The proposed method was found to be highly accurate and it successfully overcame the issues found in previous methods.

title: A Novel Multilayer Correlation Maximization Model for Improving CCA Based Frequency Recognition in SSVEP Brain Computer Interface
abstract: Multiset canonical correlation analysis (MsetCCA) has been successfully applied to optimize the reference signals by extracting common features from multiple sets of electroencephalogram (EEG) for steady state visual evoked potential (SSVEP) recognition in brain computer interface application. To avoid extracting the possible noise components as common features, this study proposes a sophisticated extension of MsetCCA, called multilayer correlation maximization (MCM) model for further improving SSVEP recognition accuracy. MCM combines advantages of both CCA and MsetCCA by carrying out three layers of correlation maximization processes. The first layer is to extract the stimulus frequency related information in using CCA between EEG samples and sine cosine reference signals. The second layer is to learn reference signals by extracting the common features with MsetCCA. The third layer is to re optimize the reference signals set in using CCA with sine cosine reference signals again. Experimental study is implemented to validate effectiveness of the proposed MCM model in comparison with the standard CCA and MsetCCA algorithms. Superior performance of MCM demonstrates its promising potential for the development of an improved SSVEP based brain computer interface.

title: Improving the Performance of SSVEP BCI with Short Response Time by Temporal Alignments Enhanced CCA
abstract: Steady State Visual Evoked Potentials (SSVEP) based Brain Computer Interface (BCI) provides high throughput in communication. In SSVEP BCI, typically, higher accuracy can be achieved with a relatively longer response time. It is therefore a research topic to reduce the response time while keeping high accuracy. We propose a new method, temporal alignments enhanced Canonical Correlation Analysis (TACCA) followed by a decision fusion to improve classification accuracy with short response time. TACCA exploits linear correlation with non linear similarity between steady state responses and stimulus frequencies. We compare TACCA and three state of the art methods using data from 54 subjects with response time ranging from 0.5 to 4 seconds. The evaluation results show that TACCA yields mean significant accuracy increase of 10 30% in all segment lengths, especially for the shorter time segment. One way ANOVA tests show high significant differences between single and multiple phases in TACCA performance.