Multiscale noise suppression and feature frequency extraction in SSVEP based on underdamped second-order stochastic resonance

Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection

OBJECTIVE

The progression of brain-computer interfaces (BCIs) has been propelled by breakthroughs in neuroscience, signal processing, and machine learning, marking it as a dynamic field of study over the past few decades. Nevertheless, the nonlinear and non-stationary characteristics of steady-state visual evoked potentials (SSVEPs), coupled with the incongruity between frequently employed linear techniques and nonlinear signal attributes, resulted in the subpar performance of mainstream non-training algorithms like canonical correlation analysis (CCA), multivariate synchronization index (MSI), and filter bank CCA (FBCCA) in short-term SSVEP detection.

METHODS

To tackle this problem, the novel fusions of common filter bank analysis, CCA dimensionality reduction methods, USSR models, and MSI recognition models are used in SSVEP signal recognition.

RESULTS

Unlike conventional linear techniques such as CCA, MSI, and FBCCA, the filter bank second-order underdamped stochastic resonance (FBUSSR) analysis demonstrates superior efficacy in the detection of short-term high-speed SSVEPs.

CONCLUSION

This research enlists 32 subjects and uses a public dataset to assess the proposed approach, and the experimental outcomes indicate that the non-training method can attain greater recognition precision and stability. Furthermore, under the conditions of the newly proposed fusion method and light stimulation, the USSR model exhibits the most optimal enhancement effect.

SIGNIFICANCE

The findings of this study underscore the expansive potential for the application of BCI systems in the realm of neuroscience and signal processing.

A novel untrained SSVEP-EEG feature enhancement method using canonical correlation analysis and underdamped second-order stochastic resonance

Objective

Compared with the light-flashing paradigm, the ring-shaped motion checkerboard patterns avoid uncomfortable flicker or brightness modulation, improving the practical interactivity of brain-computer interface (BCI) applications. However, due to fewer harmonic responses and more concentrated frequency energy elicited by the ring-shaped checkerboard patterns, the mainstream untrained algorithms such as canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods have poor recognition performance and low information transmission rate (ITR).

Methods

To address this issue, a novel untrained SSVEP-EEG feature enhancement method using CCA and underdamped second-order stochastic resonance (USSR) is proposed to extract electroencephalogram (EEG) features.

Results

In contrast to typical unsupervised dimensionality reduction methods such as common average reference (CAR), principal component analysis (PCA), multidimensional scaling (MDS), and locally linear embedding (LLE), CCA exhibits higher adaptability for SSVEP rhythm components.

Conclusion

This study recruits 42 subjects to evaluate the proposed method and experimental results show that the untrained method can achieve higher detection accuracy and robustness.

Significance

This untrained method provides the possibility of applying a nonlinear model from one-dimensional signals to multi-dimensional signals.

Multi-scale noise transfer and feature frequency detection in SSVEP based on FitzHugh-Nagumo neuron system

Objective. The steady-state visual evoked potential (SSVEP) is one of the most commonly used control signals for brain-computer interfaces (BCIs) due to its excellent interactive potential, such as high tolerance to noises and robust performance across users. In addition, it has a stable cycle, obvious characteristics and minimal training requirements. However, the SSVEP is extremely weak and companied with strong and multi-scale noise, resulting in a poor signal-to-noise ratio in practice. Common algorithms for classification are based on the principle of template matching and spatial filtering, which cannot obtain satisfied performance of SSVEP detection under the multi-scale noise. Therefore, using linear methods to extract SSVEP with obvious nonlinear and non-stationary characteristics, the useful signal will be attenuated or lost.Approach.To address this issue, two novel frameworks based on a two-dimensional nonlinear FitzHugh-Nagumo (FHN) neuron system are proposed to extract feature frequency of SSVEP.Results.In order to evaluate the effectiveness of the proposed methods, this research recruit 22 subjects to participate the experiment. Experimental results show that nonlinear FHN neuron model can force the energy of noise to be transferred into SSVEP and hence amplifying the amplitude of the target frequency. Compared with the traditional methods, the FHN and FHNCCA methods can achieve higher classification accuracy and faster processing speed, which effectively improves the information transmission rate of SSVEP-based BCI.

Waveform feature extraction and signal recovery in single-channel TVEP based on Fitzhugh-Nagumo stochastic resonance

Objective. Transient visual evoked potential (TVEP) can reflect the condition of the visual pathway and has been widely used in brain-computer interface. TVEP signals are typically obtained by averaging the time-locked brain responses across dozens or even hundreds of stimulations, in order to remove different kinds of interferences. However, this procedure increases the time needed to detect the brain status in realistic applications. Meanwhile, long repeated stimuli can vary the evoked potentials and discomfort the subjects. Therefore, a novel unsupervised framework was developed in this study to realize the fast extraction of single-channel TVEP signals with a high signal-to-noise ratio.Approach.Using the principle of nonlinear aperiodic FitzHugh-Nagumo (FHN) model, a fast extraction and signal restoration technology of TVEP waveform based on FHN stochastic resonance is proposed to achieve high-quality acquisition of signal features with less average times.Results:A synergistic effect produced by noise, aperiodic signal and nonlinear system can force the energy of noise to be transferred into TVEP and hence amplifying the useful P100 feature while suppressing multi-scale noise.Significance. Compared with the conventional average and average-singular spectrum analysis-independent component analysis(average-SSA-ICA) method, the average-FHN method has a shorter stimulation time which can greatly improve the comfort of patients in clinical TVEP detection and a better performance of TVEP waveform i.e. a higher accuracy of P100 latency. The FHN recovery method is not only highly correlated with the original signal, but also can better highlight the P100 amplitude, which has high clinical application value.

Signal processing algorithms for SSVEP-based brain computer interface: State-of-the-art and recent developments

Over past two decades, steady-state evoked potentials (SSVEP)-based brain computer interface (BCI) systems have been extensively developed. As we all know, signal processing algorithms play an important role in this BCI. However, there is no comprehensive review of the latest development of signal processing algorithms for SSVEP-based BCI. By analyzing the papers published in authoritative journals in nearly five years, signal processing algorithms of preprocessing, feature extraction and classification modules are discussed in detail. In addition, other aspects existed in this BCI are mentioned. The following key problems are solved. (1) In recent years, which signal processing algorithms are frequently used in each module? (2) Which signal processing algorithms attract more attention in recent years? (3) Which modules are the key to signal processing in BCI field? This information is very important for choosing the appropriate algorithms, and can also be considered as a reference for further research. Simultaneously, we hope that this work can provide relevant BCI researchers with valuable information about the latest trends of signal processing algorithms for SSVEP-based BCI systems.

Weak Feature Extraction and Strong Noise Suppression for SSVEP-EEG Based on Chaotic Detection Technology

Brain computer interface (BCI) is a novel communication method that does not rely on the normal neural pathway between the brain and muscle of human. It can transform mental activities into relevant commands to control external equipment and establish direct communication pathway. Among different paradigms, steady-state visual evoked potential (SSVEP) is widely used due to its certain periodicity and stability of control. However, electroencephalogram (EEG) of SSVEP is extremely weak and companied with multi-scale and strong noise. Existing algorithms for classification are based on the principle of template matching and spatial filtering, which cannot obtain satisfied performance of feature extraction under the multi-scale noise. Especially for the subjects produce weak response for external stimuli in EEG representation, i.e., BCI-Illiteracy subject, traditional algorithms are difficult to recognize the internal patterns of brain. To address this issue, a novel method based on Chaos theory is proposed to extract feature of SSVEP. The rule of this method is applying the peculiarity of nonlinear dynamics system to detect feature of SSVEP by judging the state changes of chaotic systems after adding weak EEG. To evaluate the validity of proposed method, this research recruit 32 subjects to participate the experiment. All subjects are divided into two groups according to the preliminary classification accuracy (mean acc >70% or < 70%) by canonical correlation analysis and we define the accuracy above 70% as group A (normal subjects), below 70% as group B (BCI-Illiteracy). Then, the classification accuracy and information transmission rate of two groups are verified using Chaotic theory. Experimental results show that all classification methods using in our study achieve good performance for normal subjects while chaos obtain excellent performance and significant improvements than traditional methods for BCI-Illiteracy.