Gain optimized cosine transform domain LMS algorithm for adaptive filtering of EEG

Olkkonen, H.; Pesola, P.; Valjakka, Antti; Tuomisto, Leena

doi:10.1016/s0010-4825(98)00046-8

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…Among adaptive filters the Kalman filter has a predominant role [9][10][11][12][13]. In many industrial and medical systems the least mean square (LMS) and recursive least squares (RLS) algorithms have their earned position [14,15]. A common disadvantage to all adaptive IIR structures is the relatively slow recovery from the anomalies occurring in the measurement signal such as transients, edges and other discontinuities.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Matrix/Vector Gradient Algorithm for Design of IIR Filters and ARMA Models

Olkkonen¹,

Ahtiainen²,

Järvinen³

et al. 2013

JSIP

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This work describes a novel adaptive matrix/vector gradient (AMVG) algorithm for design of IIR filters and ARMA signal models. The AMVG algorithm can track to IIR filters and ARMA systems having poles also outside the unit circle. The time reversed filtering procedure was used to treat the unstable conditions. The SVD-based null space solution was used for the initialization of the AMVG algorithm. We demonstrate the feasibility of the method by designing a digital phase shifter, which adapts to complex frequency carriers in the presence of noise. We implement the half-sample delay filter and describe the envelope detector based on the Hilbert transform filter.

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Section: Discussionmentioning

confidence: 99%

Adaptive Matrix/Vector Gradient Algorithm for Design of IIR Filters and ARMA Models

Olkkonen¹,

Ahtiainen²,

Järvinen³

et al. 2013

JSIP

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“…The time-varying autoregressive models are useful in analysis of relatively slowly changing dynamic systems. The adaptive least mean squares (LMS) algorithm has been extensively applied in the analysis of various biomedical and industrial systems [5,6]. A disadvantage of the LMS algorithm is the poor adaptation in systems with abrupt changes.…”

Section: Introductionmentioning

confidence: 99%

Least Squares Matrix Algorithm for State-Space Modelling of Dynamic Systems

Olkkonen¹,

Olkkonen²

2011

JSIP

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This work presents a novel least squares matrix algorithm (LSM) for the analysis of rapidly changing systems using state-space modelling. The LSM algorithm is based on the Hankel structured data matrix representation. The state transition matrix is updated without the use of any forgetting function. This yields a robust estimation of model parameters in the presence of noise. The computational complexity of the LSM algorithm is comparable to the speed of the conventional recursive least squares (RLS) algorithm. The knowledge of the state transition matrix enables feasible numerical operators such as interpolation, fractional differentiation and integration. The usefulness of the LSM algorithm was proved in the analysis of the neuroelectric signal waveforms

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A wavelet-based forward BSS algorithm for acoustic noise reduction and speech enhancement

2016

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Gain optimized cosine transform domain LMS algorithm for adaptive filtering of EEG

Cited by 7 publications

References 5 publications

Adaptive Matrix/Vector Gradient Algorithm for Design of IIR Filters and ARMA Models

Adaptive Matrix/Vector Gradient Algorithm for Design of IIR Filters and ARMA Models

Least Squares Matrix Algorithm for State-Space Modelling of Dynamic Systems

A wavelet-based forward BSS algorithm for acoustic noise reduction and speech enhancement

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