Movement-related potentials during the performance of a motor task I: The effect of learning and force

Yom-Tov, Elad; Grossman, Aaron W.; Inbar, G.F.

doi:10.1007/s004220100265

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…Our own studies have focused on demonstrating elementary properties of EO in a number of implementations for classic NP-hard combinatorial problems such as graph bipartitioning [8,10], 3-coloring [11], spin glasses [9], and the traveling salesperson [8]. Several other researchers have picked up on our initial results, and have successfully applied EO to problems as diverse as pattern recognition [19], signal filtering of EEG noise [24], artificial intelligence [18], and 3d spin-glass models [12,23]. Comparative studies have shown that EO holds significant promise to provide a new, alternative approach to approximate many intractable problems [8,12,18].…”

Section: Numerical Results For Eomentioning

confidence: 99%

Self-organizing Dynamics for Optimization

Boettcher

2005

Lecture Notes in Computer Science

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Motivated by noise-driven cellular automata models of selforganized criticality (SOC), a new paradigm for the treatment of hard combinatorial optimization problems is proposed. An extremal selection process preferentially advances variables in a poor local state. The ensuing dynamic process creates broad fluctuations to explore energy landscapes widely, with frequent returns to near-optimal configurations.

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Section: Numerical Results For Eomentioning

confidence: 99%

Self-organizing Dynamics for Optimization

Boettcher

2005

Lecture Notes in Computer Science

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“…Recently, the EO applications have been extended to practical hard optimization problems, such as signal filtering [108], pattern recognition [69], [70], molecular dynamics simulations [115], multi-objective optimization [26]- [28], steel hot rolling scheduling [31], complex design [96]- [100], social modeling [38] and complex network analysis [41], etc.…”

Section: Applicability Of Eo To Np-hard Problemsmentioning

confidence: 99%

Survey on computational complexity with phase transitions and extremal optimization

Zeng¹,

Lu²

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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Applying statistical mechanics to search problems in AI, decisions and optimization has been one of the powerful channels to solve NP-hard problems. Extensive analytical and experimental research has shown that the "phase transition" phenomenon in search space is often associated with the hardness of complexity. A Bak-Sneppen (BS) model based general-purpose heuristic method, called extremal optimization (EO), proposed by Boettcher and Percus from physics society may perform very well, especially near the phase transitions in compared with other optimization methods, e.g., genetic algorithm and simulated annealing, etc. To actuate more extensive investigations on this new optimization approach particularly in control, computer and optimization communities, this survey reviews the latest research results from fundamental to practice about the connection between computational complexity and phase transitions. Then, further introduces the concepts, fundamentals, algorithms and applications of EO from its capability of self-organized criticality, backbone analysis and co-evolution moving to a far-from-equilibrium state. Finally, the concluding remarks with suggested future research are illustrated.

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“…Therefore, prior to de-noising each trial was resampled at a rate of 51.2 Hz after using an appropriate anti-aliasing ®lter. The de-noised MRPs were then averaged in groups according to two factors which we have previously shown (Yom-Tov et al 2001) to in¯uence the MRP. These factors are:…”

Section: Data Processingmentioning

confidence: 99%

Movement-related potentials during the performance of a motor task II: Cerebral areas activated during learning of the task

Yom-Tov

Grossman

Inbar

2001

Biological Cybernetics

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Movement-related potentials (MRPs) recorded from the brain are thought to vary during learning of a motor task. However, since MRPs are recorded at a very low signal-to-noise ratio, it is difficult to measure these variations. In this study we attempt to remove most of the accompanying noise thus enabling the tracking of transient phenomena in MRPs recorded during learning of a motor task. Subjects performed a simple motor task which required learning. A modified version of the matching pursuit algorithm was used in order to remove a significant portion of the electroencephalographic noise overlapping the MRPs recorded in the experiment. Small groups of MRPs were then averaged according to experimental parameters. Our results show that the power of the MRPs does not decay uniformly during learning. Instead, there is a significant peak in their power after 4 or 5 repetitions of the task. This peak is noticeable especially in electrodes placed over the prefrontal region of the cortex at times subsequent to the actual movement. The observed pattern of activity may indicate problem solving related to comprehension of the force against which the user performed the task. It is possible that this problem solving occurs in the prefrontal cortex.

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Movement-related potentials during the performance of a motor task I: The effect of learning and force

Cited by 8 publications

References 9 publications

Self-organizing Dynamics for Optimization

Self-organizing Dynamics for Optimization

Survey on computational complexity with phase transitions and extremal optimization

Movement-related potentials during the performance of a motor task II: Cerebral areas activated during learning of the task

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