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Abstract:

An Electroencephalogram (EEG) is frequently corrupted by both external noise and physiological artifacts arising from bodily activities such as cardiac, ocular, and muscular processes, which significantly degrade signal quality. Conventional filtering techniques suppress external noise; however, removing physiological artifacts is challenging because their spectral characteristics overlap with those of EEG signals. In this work, a novel hybrid adaptive framework is proposed to effectively remove cardiac and ocular artifacts from EEG signals. Initially, the instantaneous frequency (IF) of EEG signals is extracted using the Hilbert transform after decomposing the signal through Variational Mode Decomposition (VMD). Adaptive selection of decomposition modes using spectral flatness and kurtosis is performed. Subsequently, a state-space Recursive Least Squares (SSRLS) algorithm with a dynamic forgetting factor is employed to estimate and suppress artifact components. Sparse regularization is applied in recursive estimation for artifact isolation. A hybrid time–frequency consistency constraint is chosen for signal preservation. It provides an adaptive Intrinsic Mode Function (IMF) selection mechanism based on correlation and frequency characteristics to selectively process contaminated components only. As a result, useful neurological information is preserved. The performance measures evaluated include mean square error (MSE), relative error (RE), normalized mean square error (NMSE), signal-to-noise ratio (SNR), gain in signal-to-artifact ratio (GSAR), and correlation coefficient (CC), with corresponding values of 0.06 µV², 0.03, 0.18 µV², 76.28 dB, 12.49, and 99.38%, respectively. Experimental validation confirms improved robustness and computational efficiency compared to conventional VMD-RLS and transform-based approaches.

Keywords:

EEG Artifact Removal,Adaptive VMD,Sparse Recursive Filtering,State-Space RLS,Non-Stationary Signal Processing,Entropy-Based Filtering.,

References:

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XIV. Massar, Hicham, Tarik Ben Drissi, Badr Nsiri, and Mohamed Miyara. "Advancements in Blind Source Separation for EEG Artifact Removal: A Comparative Analysis of Variational Mode Decomposition and Discrete Wavelet Transform Approaches." Applied Acoustics, vol. 228, 2025, article 110300. 10.1016/j.apacoust.2024.110300.
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Abstract:

Multiplier efficiency is very significant in image processing and multimedia applications, which involve numerous floating-point multiplications. Although IEEE-754 multipliers are more precise, they also require more power, hardware, and time, which are not suitable for low-power and real-time systems. Simple control logic, partial-product encoding, and error correction are methods used in approximate floating-point multipliers to enhance efficiency. They consume less hardware and power, and still provide acceptable image quality compared to their more exact counterparts, the multipliers. Therefore, they can be used in image processing applications that require error tolerance.

Keywords:

Iterative approximate floating-point multiplier (IAFPM),IEEE-754 floating-point arithmetic,image multiplication,partial product encoding,error-tolerant image processing,low-power VLSI design,multimedia image processing applications,

References:

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II. Edavoor, P. J., A. K. Samantaray, and A. D. Rahulkar. "Design of Floating Point Multiplier Using Approximate Hybrid Radix-4/Radix-8 Booth Encoder for Image Analysis." e-Prime – Advances in Electrical Engineering, Electronics and Energy, vol. 2024, article 100546, 2024. 10.1016/j.prime.2024.100546
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XVII. Saadat, H., H. Bokhari, and S. Parameswaran. "Minimally Biased Multipliers for Approximate Integer and Floating-Point Multiplication." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 40, no. 1, 2021, pp. 210–223. IEEE, 10.1109/TCAD.2018.2857262.

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