3.2 | SSVEP Feature Enhancement Method based on ERLLP
In this comprehensive study, we leverage the multi-channel signal,
designated as, which encompasses selected SSVEP signal analysis channels
namely O1, O2, Oz, PO3, PO4, POZ, PO5, and PO6. When conceptualized as
the input image and subsequently processed through the intricacies of
the Laplace pyramid in conjunction with the Enhanced-RL operator, as
delineated in Figure 2, the resultant is a meticulously sharpened and
filtered signal, termed . Notably, when the cutting-edge ERLLP algorithm
is integrated into spectral analysis, especially in realms as nuanced as
medical diagnostics and neuroscience research, this refined signalis
positioned as the focal point of analysis, serving as a pivotal analyzed
signal.
Drawing a comparison to prevailing methodologies outlined in literature
[5], where the Prewitt, Sobel, and Laplace operators are
predominantly employed, our research pivots towards utilizing the
Enhanced-RL operator. This approach not only serves as an innovative
filtering template, tailored for gaze target recognition in the SSVEP
BCI paradigm, but also seamlessly facilitates hierarchical filtering.
Intriguingly, pyramid images cultivated at diverse hierarchical levels
exhibit variances in resolution and intricate detail information. The
granularity of this detail information becomes increasingly conspicuous
as one navigates towards the base of the pyramid. Given this gradation,
higher pyramid tiers inherently dictate the selection of an augmented
fractional order of differentiation. As the research progresses, there’s
a synthesis of this advanced sharpened filtering technique with renowned
methods such as CCA, FBCCA, and TRCA. This amalgamation births the novel
methodologies christened as ERLLP_CCA, ERLLP_FBCCA, and ERLLP_TRCA,
visually elucidated in Figure 3.
To dissect the analytical approach further, initially, the
discrimination coefficients of the pristine test signal , as calculated
through CCA, FBCCA, and TRCA methodologies, are labeled as. Subsequent
to this, the ERLLP algorithm is harnessed to refine X. Post this
enhancement, the discrimination coefficients of the sharpened and
meticulously filtered signal, as derived from CCA, FBCCA, and TRCA
techniques, are christened as. Culminating the analysis, the ERLLP_CCA,
ERLLP_FBCCA, and ERLLP_TRCA methodologies harness an integration of
the coefficients fromand, presenting them as the ultimate discrimination
coefficients, aptly captured in the ensuing mathematical equation:
| Experimental Results and Discussion
4.1 | Signal Processing
Figure 4 provides a visual elucidation of the transformative impact of
ERLLP on SSVEP signals, offering a comparative analysis of waveforms
before and after the application of ERLLP as a sharpening mechanism for
the SSVEP signals.
In Panel 4-A, the depicted waveform represents the innate
characteristics of the unaltered SSVEP signal. This original waveform
prominently showcases significant fluctuations, where the signal
intermittently deviates from its baseline, leading to an inconsistent
distribution over time. This pronounced deviation, termed as the ’trend
term’, carries substantial weight, as it profoundly impacts the overall
efficacy and interpretability of the signal.
Transitioning to Panel 4-B, a striking contrast is observed. Presented
here is the waveform of the SSVEP signal post its refinement through the
ERLLP algorithm. The resultant waveform demonstrates a remarkable
stability, underscoring the prowess of ERLLP in adeptly mitigating the
previously observed trend term inherent to the SSVEP signal. This not
only enhances the clarity and consistency of the waveform but also
underscores the algorithm’s efficacy. Additionally, the refined waveform
subtly alludes to another advantageous trait of ERLLP: its capability to
attenuate extraneous noise in the SSVEP signal, further bolstering the
signal’s integrity and reliability for subsequent analytical endeavors.