Recently, the use of brain-computer interface (BCI) systems based on Steady-State Visual Evoked Potentials (SSVEPs) has increased significantly. This increase is attributed to their advantageous features, such as acce...
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ISBN:
(纸本)9798331529710
Recently, the use of brain-computer interface (BCI) systems based on Steady-State Visual Evoked Potentials (SSVEPs) has increased significantly. This increase is attributed to their advantageous features, such as acceptable accuracy and minimal user training requirements. SSVEPs, a crucial pattern in BCI systems, are generated in the occipital region of the brain through visual stimulation within the frequency range of 6 to 60 Hz. In this paper, we propose two novel algorithms based on the Multiway Canonical Correlation Analysis (MCCA) method for frequency detection in SSVEP-based BCI systems. Our goal in the proposed algorithms, namely Sine-Cosine Filtered-CP-CCA (SCF-CP-CCA) and EEG-Filtered-CP-CCA (EF-CP-CCA), is to augment the initial reference signal by using tensor decomposition tools on multidimensional input EEG data. In both algorithms, we perform a modified Canonical Polyadic (CP) decomposition using an Alternating Least Squares (ALS) approach. Similar to the MCCA algorithm, for each stimulation frequency, a higher order array is generated using training data and the resulting tensor is decomposed using the proposed ALS method where loading matrices are filtered in each iteration. The obtained temporal loading matrix and a classic Sine-Cosine signal are then used in a Canonical Correlation Analysis (CCA) model to obtain the reference signal. The way the reference signal is generated, using the canonical scores of the EEG loading matrix or the Sine-Cosine matrix, leads to the proposal of two algorithms: SCF-CP-CCA and EF-CP-CCA. Finally, the Multiple Linear Regression (MLR) algorithm between the EEG test data matrix and the obtained reference signal is used to determine the target frequency. To evaluate the proposed algorithms, we use the real MEMANSSVEP dataset and compare the results of the proposed methods with those of the MCCA one. Compared to the MCCA algorithm, the average accuracy of the proposed SCF-CPCCA and EF-CP-CCA algorithms has improved by 3.
To address the design and functional analysis needs of radar echo simulation systems in complex environments, this paper proposes a design method for reconfigurable distributed radar echo simulation software based on ...
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This article presents a comprehensive and in-depth analysis of the integration of reconfigurable Intelligent Surfaces (RIS) with Non-Terrestrial Networks (NTN), offering ground-breaking advancements in communication s...
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mmWave massive MIMO systems will be used extensively in future communications systems to enable the increasing demand for high data rates. In such systems, hybrid precoders are preferred to fully digital precoders to ...
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As wireless communication systems continue to evolve, the study of Radio Frequency (RF) Power Amplifiers (PAs) has attracted growing interest. Addressing the modeling issues of PAs can provide more convenient research...
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This paper addresses classical simulations in the assessment of quantum computing performance. It emphasises the significance of these simulations in understanding quantum systems and exploring the potential of quantu...
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ISBN:
(纸本)9783031492686;9783031492693
This paper addresses classical simulations in the assessment of quantum computing performance. It emphasises the significance of these simulations in understanding quantum systems and exploring the potential of quantum algorithms. The challenges posed by the exponential growth of quantum states and the limitations of full-state simulations are addressed. Various approximation techniques and encoding methods are pointed out to enable simulations of larger quantum systems, and advanced simulation strategies tailored to specific goals are also discussed. This work focuses on the feasibility of classical simulation in decision processes regarding the development of software solutions, extending the assessment beyond high-performance computing systems to include standard hardware. This opportunity can foster the adoption of classical simulations of quantum algorithms to a wider range of users.
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Recent advancements in artificial intelligence (AI) allow for more sophisticated modeling and simulation of complex engineeringsystems. This research explores the application of AI techniques like neural networks and...
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Neural community-based, totally machine-getting-to-know algorithms provide an effective device for predicting most cancers in recommender systems. These algorithms leverage large datasets of affected persons' medi...
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