Query: tacs     ssvep

title: A Single Channel SSVEP Based BCI Speller Using Deep Learning
abstract: This paper aims to develop a speller system based on a bipolar single channel electroencephalogram with sufficient accuracy. The proposed system consists of a custom designed headset, a new virtual keyboard with 58 characters, special symbols, and digits, and a five target steady state visual evoked potential (SSVEP) based brain computer interface (BCI) utilizing one dimensional convolutional neural network (1 D CNN) for SSVEP frequency detection. The deep learning model is implemented and trained under the training mode before being applied in the operation mode of the system. To validate the proposed model, we acquire the training dataset with numerous testing conditions, including different frequency resolutions of the feature and different time window lengths of analysis. Two types of features based on the frequency domain are investigated to compare their performances in terms of classification accuracy of the model. The experimental results from eight subjects shows that on average, the proposed model can classify five class SSVEP data with a high accuracy of 99.2% The proposed BCI is then employed in an online experiment of spelling the word "SPELLER" using 2 s time window. Consequently, the system achieves an average accuracy of 97.4% and an information transfer rate of 49 7.7 bpm, showing the practicality and feasibility of implementing a reliable single channel SSVEP based speller utilizing 1 D CNN.

title: Combination of high frequency SSVEP based BCI and computer vision for controlling a robotic arm.
abstract: Recent attempts in developing brain computer interface (BCI) controlled robots have shown the potential of this area in the field of assistive robots. However, implementing the process of picking and placing objects using a BCI controlled robotic arm still remains challenging. BCI performance, system portability, and user comfort need to be further improved.  In this study, a novel control approach, which combines high frequency steady state visual evoked potential (SSVEP) based BCI and computer vision based object recognition, is proposed to control a robotic arm for performing pick and place tasks that require control with multiple degrees of freedom. The computer vision can identify objects in the workspace and locate their positions, while the BCI allows the user to select one of these objects to be acted upon by the robotic arm. The robotic arm was programmed to be able to autonomously pick up and place the selected target object without moment by moment supervision by the user.  Online results obtained from ten healthy subjects indicated that a BCI command for the proposed system could be selected from four possible choices in 6.5 s (i.e. 2.25 s for visual stimulation and 4.25 s for gaze shifting) with 97.75% accuracy. All subjects could successfully complete the pick and place tasks using the proposed system.  These results demonstrated the feasibility and efficiency of combining high frequency SSVEP based BCI and computer vision based object recognition to control robotic arms. The control strategy presented here could be extended to control robotic arms to perform other complicated tasks.

title: Simulating Brain Signals: Creating Synthetic EEG Data via Neural Based Generative Models for Improved SSVEP Classification
abstract: Despite significant recent progress in the area of Brain Computer Interface (BCI) there are numerous shortcomings associated with collecting Electroencephalography (EEG) signals in real world environments. These include, but are not limited to, subject and session data variance, long and arduous calibration processes and predictive generalisation issues across different subjects or sessions. This implies that many downstream applications, including Steady State Visual Evoked Potential (SSVEP) based classification systems, can suffer from a shortage of reliable data. Generating meaningful and realistic synthetic data can therefore be of significant value in circumventing this problem. We explore the use of modern neural based generative models trained on a limited quantity of EEG data collected from different subjects to generate supplementary synthetic EEG signal vectors, subsequently utilised to train an SSVEP classifier. Extensive experimental analysis demonstrates the efficacy of our generated data, leading to improvements across a variety of evaluations, with the crucial task of cross subject generalisation improving by over 35% with the use of such synthetic data.

title: Double blind, randomized pilot clinical trial targeting alpha oscillations with transcranial alternating current stimulation (tACS) for the treatment of major depressive disorder (MDD)
abstract: Major depressive disorder (MDD) is one of the most common psychiatric disorders, but pharmacological treatments are ineffective in a substantial fraction of patients and are accompanied by unwanted side effects. Here we evaluated the feasibility and efficacy of transcranial alternating current stimulation (tACS) at 10 Hz, which we hypothesized would improve clinical symptoms by renormalizing alpha oscillations in the left dorsolateral prefrontal cortex (dlPFC) To this end, 32 participants with MDD were randomized to 1 of 3 arms and received daily 40 min sessions of either 10 Hz tACS, 40 Hz tACS, or active sham stimulation for 5 consecutive days. Symptom improvement was assessed using the Montgomery Asberg Depression Rating Scale (MADRS) as the primary outcome. High density electroencephalograms (hdEEGs) were recorded to measure changes in alpha oscillations as the secondary outcome. For the primary outcome, we did not observe a significant interaction between treatment condition (10 Hz tACS, 40 Hz tACS, sham) and session (baseline to 4 weeks after completion of treatment) however, exploratory analyses show that 2 weeks after completion of the intervention, the 10 Hz tACS group had more responders (MADRS and HDRS) compared with 40 Hz tACS and sham groups (n 30, p 0.026) Concurrently, we found a significant reduction in alpha power over the left frontal regions in EEG after completion of the intervention for the group that received per protocol 10 Hz tACS (n 26, p 0.05) Our data suggest that targeting oscillations with tACS has potential as a therapeutic intervention for treatment of MDD.

title: Learning across multi stimulus enhances target recognition methods in SSVEP based BCIs.
abstract: <i>Objective</i> Latest target recognition methods that are equipped with learning from subject's calibration data, represented by the extended canonical correlation analysis (eCCA) and the ensemble task related component analysis (eTRCA) can achieve extra high performance in the steady state visual evoked potential (SSVEP) based brain computer interfaces (BCIs) however their performance deteriorate drastically if the calibration trials are insufficient. This paper develops a new scheme to learn from limited calibration data. <i>Approach</i> A learning across multiple stimuli scheme is proposed for the target recognition methods, which applies to learning the data corresponding to not only the target stimulus but also the other stimuli. The resulting optimization problems can be simplified and solved utilizing the prior knowledge and properties of SSVEPs across different stimuli. With the new learning scheme, the eCCA and the eTRCA can be extended to the multi stimulus eCCA (ms eCCA) and the multi stimulus eTRCA (ms eTRCA) respectively, as well as a combination of them (i.e. ms eCCA ms eTRCA) that incorporates their merits. <i>Main results</i> Evaluation and comparison using an SSVEP BCI benchmark dataset with 35 subjects show that the ms eCCA (or ms eTRCA) performs significantly better than the eCCA (or eTRCA) method while the ms eCCA+ms eTRCA performs the best. With the learning across stimuli scheme, the existing target recognition methods can be further improved in terms of the target recognition performance and the ability against insufficient calibration. <i>Significance</i> A new learning scheme is proposed towards efficient use of the calibration data, providing enhanced performance and saving calibration time in the SSVEP based BCIs.

title: On the Relative Contribution of Deep Convolutional Neural Networks for SSVEP Based Bio Signal Decoding in BCI Speller Applications
abstract: Brain computer interfaces (BCI) harnessing steady state visual evoked potentials (SSVEPs) manipulate the frequency and phase of visual stimuli to generate predictable oscillations in neural activity. For BCI spellers, oscillations are matched with alphanumeric characters allowing users to select target numbers and letters. Advances in BCI spellers can, in part, be accredited to subject specific optimization, including; 1) custom electrode arrangements; 2) filter sub band assessments; and 3) stimulus parameter tuning. Here, we apply deep convolutional neural networks (DCNNs) demonstrating cross subject functionality for the classification of frequency and phase encoded SSVEP. Electroencephalogram (EEG) data are collected and classified using the same parameters across subjects. Subjects fixate forty randomly cued flickering characters \textsf {5} \times \textsf {8} keyboard array) during concurrent wet EEG acquisition. These data are provided by an open source SSVEP dataset. Our proposed DCNN, PodNet, achieves 86% and 77% offline accuracy of classification across subjects for two data capture periods, respectively, 6 seconds (information transfer rate 40 bpm) and 2 seconds (information transfer rate 101 bpm) Subjects demonstrating sub optimal <70% performance are classified to similar levels after a short subject specific training period. PodNet outperforms filter bank canonical correlation analysis for a low volume (3 channel) clinically feasible occipital electrode configuration. The networks defined in this study achieve functional performance for the largest number of SSVEP classes decoded via DCNN to date. Our results demonstrate PodNet achieves cross subject, calibrationless classification and adaptability to sub optimal subject data, and low volume EEG electrode arrangements.

title: Towards a Hybrid BCI Gaming Paradigm Based on Motor Imagery and SSVEP
abstract: Brain computer interfaces (BCIs) not only can allow individuals to voluntarily control external devices, helping to restore lost motor functions of the disabled, but can also be used by healthy users for entertainment and gaming applications. In this study, we proposed a hybrid BCI paradigm to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. In this paradigm, we combined motor imagery (MI) and steady state visually evoked potentials (SSVEPs) to generate multiple commands. A classic game, Tetris, was chosen as the control object. The novelty of this study includes the effective usage of a "dwell time" approach and fusion rules to design BCI games. To demonstrate the feasibility of the proposed hybrid paradigm, ten subjects were chosen to participate in online control experiments. The experimental results showed that all subjects successfully completed the predefined tasks with high accuracy. This proposed hybrid BCI paradigm could potentially provide those who suffer disability or paralysis with additional entertainment options, such as brain actuated games, that could improve their happiness and quality of life.  BCI: brain computer interface; EEG: electroencephalogram; MI: motor imagery; SSVEP: steady state visually evoked potential; ERP: event related potential; SMR: sensorimotor rhythm; VEP: visual evoked potential; TCP/IP: transmission control protocol/internet protocol; GUI: graphical user interface; ERD/ERS: event related desynchronization/synchronization; CIC: control intention classifier; LRC: left/right classifier; CSP: common spatial pattern; LDA: linear discriminant analysis; ROC: receiver operating characteristic; TPR: true positive rate; FPR: false positive rate; CCA: canonical correlation analysis.

title: A Bayesian Multiple Kernel Learning Algorithm for SSVEP BCI Detection
abstract: This paper deals with the classification of steady state visual evoked potentials (SSVEP) which is a multiclass classification problem addressed in SSVEP based brain computer interfaces. In particular, our method named MultiLRM_MKL uses multiple linear regression models under a Sparse Bayesian Learning (SBL) framework to discriminate between the SSVEP classes. The regression coefficients of each model are learned using the Variational Bayesian (VB) framework and the kernel trick is adopted not only for reducing the computational cost of our method, but also for enabling the combination of different kernel spaces. We verify the effectiveness of our method in handling different kernel spaces by evaluating its performance with a new kernel based on canonical correlation analysis. In particular, we prove the benefit of combining multiple kernels by outperforming several state of the art methods in two SSVEP datasets, reaching an information transfer rate of 93 b/min using only three channels from the occipital area <inline formula><tex math notation="LaTeX"$O_z$/tex math>/inline formula> <inline formula><tex math notation="LaTeX"$O_1$/tex math>/inline formula> and <inline formula><tex math notation="LaTeX"$O_2$/tex math>/inline formula>

title: A Wearable SSVEP Based BCI System for Quadcopter Control Using Head Mounted Device
abstract: Restoring the interaction between disabled people and the 3 D physical world via a brain computer interface (BCI) is an exciting topic. To this end, we designed a wearable BCI system based on the steady state visual evoked potential (SSVEP) which enables 3 D navigation of quadcopter flight with immersive first person visual feedback using a head mounted device. In addition, to alleviate the user's operational burden, this paper provides asynchronous switch control for the users. The transitional state due to head movement in an asynchronous BCI was isolated online and translated into hover to eliminate its influence. The experimental results in the physical environment showed that the subjects could accomplish the 3 D flight tasks accurately and smoothly using our system. In particular, in this paper, we proposed an information transfer rate metric that is suitable for the asynchronous task. We demonstrated the feasibility of using the head mounted device and a proper control strategy to facilitate the portability and practicability of the SSVEP based BCI system for its navigation utility.

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.