513 research outputs found
An Adaptive Task-Related Component Analysis Method for SSVEP recognition
Steady-state visual evoked potential (SSVEP) recognition methods are equipped
with learning from the subject's calibration data, and they can achieve extra
high performance in the SSVEP-based brain-computer interfaces (BCIs), however
their performance deteriorate drastically if the calibration trials are
insufficient. This study develops a new method to learn from limited
calibration data and it proposes and evaluates a novel adaptive data-driven
spatial filtering approach for enhancing SSVEPs detection. The spatial filter
learned from each stimulus utilizes temporal information from the corresponding
EEG trials. To introduce the temporal information into the overall procedure,
an multitask learning approach, based on the bayesian framework, is adopted.
The performance of the proposed method was evaluated into two publicly
available benchmark datasets, and the results demonstrated that our method
outperform competing methods by a significant margin.Comment: 23 pages, 3 Figures, 6 Table
Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis
Canonical correlation analysis (CCA) has been one of the most popular methods
for frequency recognition in steady-state visual evoked potential (SSVEP)-based
brain-computer interfaces (BCIs). Despite its efficiency, a potential problem
is that using pre-constructed sine-cosine waves as the required reference
signals in the CCA method often does not result in the optimal recognition
accuracy due to their lack of features from the real EEG data. To address this
problem, this study proposes a novel method based on multiset canonical
correlation analysis (MsetCCA) to optimize the reference signals used in the
CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple
linear transforms that implement joint spatial filtering to maximize the
overall correlation among canonical variates, and hence extracts SSVEP common
features from multiple sets of EEG data recorded at the same stimulus
frequency. The optimized reference signals are formed by combination of the
common features and completely based on training data. Experimental study with
EEG data from ten healthy subjects demonstrates that the MsetCCA method
improves the recognition accuracy of SSVEP frequency in comparison with the CCA
method and other two competing methods (multiway CCA (MwayCCA) and phase
constrained CCA (PCCA)), especially for a small number of channels and a short
time window length. The superiority indicates that the proposed MsetCCA method
is a new promising candidate for frequency recognition in SSVEP-based BCIs
Bio-Inspired Filter Banks for SSVEP-based Brain-Computer Interfaces
Brain-computer interfaces (BCI) have the potential to play a vital role in
future healthcare technologies by providing an alternative way of communication
and control. More specifically, steady-state visual evoked potential (SSVEP)
based BCIs have the advantage of higher accuracy and higher information
transfer rate (ITR). In order to fully exploit the capabilities of such
devices, it is necessary to understand the features of SSVEP and design the
system considering its biological characteristics. This paper introduces
bio-inspired filter banks (BIFB) for a novel SSVEP frequency detection method.
It is known that SSVEP response to a flickering visual stimulus is frequency
selective and gets weaker as the frequency of the stimuli increases. In the
proposed approach, the gain and bandwidth of the filters are designed and tuned
based on these characteristics while also incorporating harmonic SSVEP
responses. This method not only improves the accuracy but also increases the
available number of commands by allowing the use of stimuli frequencies elicit
weak SSVEP responses. The BIFB method achieved reliable performance when tested
on datasets available online and compared with two well-known SSVEP frequency
detection methods, power spectral density analysis (PSDA) and canonical
correlation analysis (CCA). The results show the potential of bio-inspired
design which will be extended to include further SSVEP characteristic (e.g.
time-domain waveform) for future SSVEP based BCIs.Comment: 2016 IEEE International Conference on Biomedical and Health
Informatics (BHI
Cross-subject dual-domain fusion network with task-related and task-discriminant component analysis enhancing one-shot SSVEP classification
This study addresses the significant challenge of developing efficient
decoding algorithms for classifying steady-state visual evoked potentials
(SSVEPs) in scenarios characterized by extreme scarcity of calibration data,
where only one calibration is available for each stimulus target. To tackle
this problem, we introduce a novel cross-subject dual-domain fusion network
(CSDuDoFN) incorporating task-related and task-discriminant component analysis
(TRCA and TDCA) for one-shot SSVEP classification. The CSDuDoFN framework is
designed to comprehensively transfer information from source subjects, while
TRCA and TDCA are employed to exploit the single available calibration of the
target subject. Specifically, we develop multi-reference least-squares
transformation (MLST) to map data from both source subjects and the target
subject into the domain of sine-cosine templates, thereby mitigating
inter-individual variability and benefiting transfer learning. Subsequently,
the transformed data in the sine-cosine templates domain and the original
domain data are separately utilized to train a convolutional neural network
(CNN) model, with the adequate fusion of their feature maps occurring at
distinct network layers. To further capitalize on the calibration of the target
subject, source aliasing matrix estimation (SAME) data augmentation is
incorporated into the training process of the ensemble TRCA (eTRCA) and TDCA
models. Ultimately, the outputs of the CSDuDoFN, eTRCA, and TDCA are combined
for SSVEP classification. The effectiveness of our proposed approach is
comprehensively evaluated on three publicly available SSVEP datasets, achieving
the best performance on two datasets and competitive performance on one. This
underscores the potential for integrating brain-computer interface (BCI) into
daily life.Comment: 10 pages,6 figures, and 3 table
Review of real brain-controlled wheelchairs
This paper presents a review of the state of the art regarding wheelchairs driven by a brain-computer interface (BCI). Using a brain-controlled wheelchair (BCW), disabled users could handle a wheelchair through their brain activity, granting autonomy to move through an experimental environment. A classification is established, based on the characteristics of the BCW, such as the type of electroencephalographic (EEG) signal used, the navigation system employed by the wheelchair, the task for the participants, or the metrics used to evaluate the performance. Furthermore, these factors are compared according to the type of signal used, in order to clarify the differences among them. Finally, the trend of current research in this field is discussed, as well as the challenges that should be solved in the future
Oscillatory Source Tensor Discriminant Analysis (OSTDA): A regularized tensor pipeline for SSVEP-based BCI systems
Periodic signals called Steady-State Visual Evoked Potentials (SSVEP) are elicited in the brain by flickering stimuli. They are usually detected by means of regression techniques that need relatively long trial lengths to provide feedback and/or sufficient number of calibration trials to be reliably estimated in the context of brain-computer interface (BCI). Thus, for BCI systems designed to operate with SSVEP signals, reliability is achieved at the expense of speed or extra recording time. Furthermore, regardless of the trial length, calibration free regression-based methods have been shown to suffer from significant performance drops when cognitive perturbations are present affecting the attention to the flickering stimuli. In this study we present a novel technique called Oscillatory Source Tensor Discriminant Analysis (OSTDA) that extracts oscillatory sources and classifies them using the newly developed tensor-based discriminant analysis with shrinkage. The proposed approach is robust for small sample size settings where only a few calibration trials are available. Besides, it works well with both low- and high-number-of-channel settings, using trials as short as one second. OSTDA performs similarly or significantly better than other three benchmarked state-of-the-art techniques under different experimental settings, including those with cognitive disturbances (i.e. four datasets with control, listening, speaking and thinking conditions). Overall, in this paper we show that OSTDA is the only pipeline among all the studied ones that can achieve optimal results in all analyzed conditions
On Tackling Fundamental Constraints in Brain-Computer Interface Decoding via Deep Neural Networks
A Brain-Computer Interface (BCI) is a system that provides a communication and control medium between human cortical signals and external devices, with the primary aim to assist or to be used by patients who suffer from a neuromuscular disease. Despite significant recent progress in the area of BCI, there are numerous shortcomings associated with decoding Electroencephalography-based BCI signals in real-world environments. These include, but are not limited to, the cumbersome nature of the equipment, complications in collecting large quantities of real-world data, the rigid experimentation protocol and the challenges of accurate signal decoding, especially in making a system work in real-time. Hence, the core purpose of this work is to investigate improving the applicability and usability of BCI systems, whilst preserving signal decoding accuracy.
Recent advances in Deep Neural Networks (DNN) provide the possibility for signal processing to automatically learn the best representation of a signal, contributing to improved performance even with a noisy input signal. Subsequently, this thesis focuses on the use of novel DNN-based approaches for tackling some of the key underlying constraints within the area of BCI. For example, recent technological improvements in acquisition hardware have made it possible to eliminate the pre-existing rigid experimentation procedure, albeit resulting in noisier signal capture. However, through the use of a DNN-based model, it is possible to preserve the accuracy of the predictions from the decoded signals. Moreover, this research demonstrates that by leveraging DNN-based image and signal understanding, it is feasible to facilitate real-time BCI applications in a natural environment. Additionally, the capability of DNN to generate realistic synthetic data is shown to be a potential solution in reducing the requirement for costly data collection. Work is also performed in addressing the well-known issues regarding subject bias in BCI models by generating data with reduced subject-specific features.
The overall contribution of this thesis is to address the key fundamental limitations of BCI systems. This includes the unyielding traditional experimentation procedure, the mandatory extended calibration stage and sustaining accurate signal decoding in real-time. These limitations lead to a fragile BCI system that is demanding to use and only suited for deployment in a controlled laboratory. Overall contributions of this research aim to improve the robustness of BCI systems and enable new applications for use in the real-world
Data Analytics in Steady-State Visual Evoked Potential-based Brain-Computer Interface: A Review
Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signal-to-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed
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