Abstract
Steady-State Visual Evoked Potentials (SSVEP) have garnered significant attention due to their promising applications in brain-computer interfaces (BCI), medical diagnostics, and several other domains. Enhancing the characteristics of SSVEP signals through intricate signal processing has emerged as a pivotal research focus for more efficient signal extraction. In this work, we introduce a novel layered enhancement algorithm for SSVEP electroencephalogram (SSVEP-EEG) signals based on fractional-order differentiation operators. This innovative approach marries brain signal analysis with image processing methodologies. By utilizing fractional-order differentiation operators in tandem with the Laplace pyramid, the signal undergoes hierarchical enhancement. This amplified signal is then reconstructed, which facilitates an in-depth extraction of image intricacies and attributes, ultimately accentuating the distinctiveness of SSVEP features. To validate the efficacy of the proposed method, we applied it to three recognized target identification algorithms: Canonical Correlation Analysis (CCA), Filter Bank Canonical Correlation Analysis (FBCCA), and Task-Related Component Analysis (TRCA) using a publicly available dataset. Experimental outcomes underscore that, in contrast to contemporary techniques, our proposed algorithm not only effectively attenuates the trend components of SSVEP signals but also substantially elevates the recognition precision of CCA, FBCCA, and TRCA.
| Introduction
Over the past three decades, Brain-Computer Interface (BCI) technology has garnered significant scholarly attention. This advanced framework facilitates direct interaction between the human brain’s activity and computing systems, offering a novel avenue for encoding and decoding information predicated solely on neural activity [1]. Among various BCI modalities, the Steady-State Visual Evoked Potential (SSVEP) has emerged as a particularly salient methodology, largely because SSVEP-based BCI systems obviate the need for extensive user training [2] and boast high information transfer rates [3]. As such, it’s increasingly recognized as a promising modality for interactive applications, often underpinning the development of sophisticated systems [4]. One of the paramount applications of SSVEP is within the realm of BCI, where the precise identification of a user’s intent stands as a pivotal research direction [5, 6, 7, 8]. In SSVEP-based BCIs, an array of flashing modules, each oscillating at distinct frequencies, is used as stimuli, where each stimulus corresponds to a specific operational command [9]. When a user fixates on a particular stimulus, cortical neural activities get modulated, resulting in the generation of periodic rhythms that resonate at the same frequency as the stimulus. This activity is predominantly localized in the occipital region of the cortex [10]. By pinpointing the peak frequencies within the induced electroencephalographic signals, one can discern the specific stimulus the user is focusing on, thereby inferring the user’s intent [11]. Several algorithms have been developed to enhance target recognition within this context, including Canonical Correlation Analysis (CCA) [12], Filter Bank Canonical Correlation Analysis (FBCCA) [13], Multivariate Synchronization Index (MSI) [14], Individual Template-based Canonical Correlation Analysis (IT-CCA) [15], Multiway Canonical Correlation Analysis (Mway CCA) [16], and Task-Related Component Analysis (TRCA) [17], among others.
Nevertheless, the inherent low signal-to-noise ratio of SSVEP signals poses a formidable challenge, constraining its research, analysis, and application. To enhance the extraction efficiency of SSVEP signals, it becomes imperative to augment their characteristics. Yan et al.[18] introduced an Image Filtering Denoising (IFD) method for SSVEP, initially filtering multi-channel EEG signals to retrieve noise, which is then subtracted from the original signal to procure the denoised version. This approach essentially delineates blurred details from an image and then excludes them to obtain finer details. However, this method tends to inadvertently discard some essential information in the process.
To overcome this impediment, we propose an Enhanced Feature Augmentation Algorithm based on Fractional-Order Derivative Operator, termed Enhanced Riemann-Liouville based on Laplacian Pyramid (ERLLP), to amplify SSVEP Electroencephalogram (SSVEP-EEG) signals. The strategy first employs a sharpening filter utilizing spatial differencing to extract image intricacies, followed by an application of the fractional-order derivative operator for optimal detail and contour refinement. Contrasting the integer-order differential operators, like Prewitt, Sobel, and Laplace, used in [5], fractional-order derivatives, as an extension, not only offer image sharpening but, due to their weak derivative properties, can also bolster the high-frequency components while preserving the low-frequency portions in a nonlinear fashion. This makes fractional-order derivatives more potent in analyzing and enhancing image characteristics. Hence, we integrated the fractional-order derivative operator and further refined the traditional Riemann-Liouville fractional derivative operator, leading to the inception of the Enhanced-RL operator. Subsequently, employing the Laplacian pyramid facilitates layered signal processing, followed by signal reconstruction to further delineate details and features. Ultimately, using a public dataset, the study corroborates the augmented efficacy of our ERLLP algorithm in enhancing the recognition capabilities of user intent identification algorithms like CCA, FBCCA, and TRCA. Experimental outcomes underscore that, in contrast to the IFD and the methodologies detailed in [5], our proposed ERLLP algorithm markedly escalates the accuracy of SSVEP signal recognition.
| Theoretical Framework
| Enhanced-RL Operator
Given the foundational work on the Riemann-Liouville (RL) differential, Ni et al. [19] advanced the field by introducing a specific image enhancement operator. Recognizing the computational challenges associated with higher-order complexities, they strategically executed differentiation operations, bifurcating them along both the positive and negative directions on theandaxes. Additionally, differentiation was executed across the four cardinal diagonal vectors. To facilitate this refined operation, a 3×3 template was judiciously deployed. Subsequently, the products of these eight distinctive fractional differential templates, derived from varied orientations, were amalgamated. The culmination of this intricate process resulted in a 5×5 convolution template, a depiction of which is presented in Figure 1: