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High frequency subthalamic nucleus stimulation
Published in Hans O Lüders, Deep Brain Stimulation and Epilepsy, 2020
Erwin B Montgomery, John T Gale, Kenneth B Baker
There is another consequence of misinformation that is an excessive driving or gain of function causing positive symptoms such as abnormal involuntary movements. This excessive driving or gain of function can be a consequence of stochastic resonance. This counterintuitive notion relates to an improvement in the signal-to-noise ratio (information content relative to noise) when noise is added.
Augmenting Attention with Brain–Computer Interfaces
Published in Chang S. Nam, Anton Nijholt, Fabien Lotte, Brain–Computer Interfaces Handbook, 2018
Mehdi Ordikhani-Seyedlar, Mikhail A. Lebedev
Attention-related oscillations occur in the γ-band (30–80 Hz) and even higher-frequency bands (Crone et al. 2006). Ray et al. (2008) observed high-γ activity (80–150 Hz) in subjects presented with a sequence of auditory and tactile stimuli and instructed to attend to one of these modalities. Attentional shifts between the modalities resulted in high-γ activity in the cortical areas that correspond to the chosen modality, that is, auditory cortex for sounds and somatosensory cortex for tactile sensations. Furthermore, high-γ activity was elevated in PFC when subjects attended to any modality, which agrees with the suggestion that PFC is a part of the supramodal attentional system (Dirlikov et al. 2015; Keune et al. 2015). Oscillations at 350 Hz were reported in human frontal and centro-parietal regions, where they occurred in response to somatosensory stimulation (Ozaki et al. 2006). Several explanations have been proposed for the function of high-frequency oscillations during attentional shifts. According to one hypothesis, ultrahigh-frequency oscillations represent noise that has a modulatory function in neural processing (Benzi et al. 1982). Adding moderate amounts of noise to the activity of a brain circuit increases neural synchrony and decreases stimulus detection threshold (Ward et al. 2006), the effect known as stochastic resonance (Benzi et al. 1982). Similar modulations of brain circuits can be produced by adding noise to the brain activity using microstimulation (Medina et al. 2012).
Energy Medicine: Focus on Nonthermal Electromagnetic Therapies
Published in Len Wisneski, The Scientific Basis of Integrative Health, 2017
Len Wisneski, Bernard O. Williams
Noise is the underlying problem in any attempt to explain biological and clinical responses of human tissues to weak electromagnetic fields. Even for models using triggering signals, rather than power-driving models, the signal-to-noise ratio governs the signal detection at the molecular/cellular/tissue target in the presence of thermal noise. Stochastic resonance is a mechanism that provides signal amplification in a thermally noisy environment. Electronic devices and living systems can detect signals that are much smaller than ambient noise. The key to understanding these processes is the nonlinear character of the systems. Noise itself plays a constructive role in detecting weak rhythmic signals. Regular, periodic signals can entrain the ambient noise, boosting the signal strength to a detectable level (Bulsara and Gammaitoni, 1996; Oschman, 2004; Wiesenfeld and Moss, 1995).
Current perspectives on galvanic vestibular stimulation in the treatment of Parkinson’s disease
Published in Expert Review of Neurotherapeutics, 2021
Soojin Lee, Aiping Liu, Martin J. McKeown
RN-GVS utilizes a broadband white noise or pink noise with a 32]. This may allow for augmenting the activity of the vestibular system without severely disrupting it. One of the justifications to explain how the randomly varying stimuli may provide beneficial effects is the stochastic resonance phenomenon, where a sub-threshold random stimulus paradoxically enhances sensory information processing and perception [33]. For example, 40 Hz responses of the human auditory cortex to auditory stimuli are enhanced when weak noise is added to the stimulus [34]. In PD studies, RN-GVS has been the most extensively investigated, and several beneficial effects on postural control, motor function, and neural activity have been reported (described in sections 4 and 5 in detail).
Pain reduction in validated rat pain models: radio frequency spectrum targeted at the low and ultra-low ends using the emulate® delivery system
Published in Electromagnetic Biology and Medicine, 2022
Xavier A. Figueroa, Lucas Lacambra, B. Michael Butters
A possibility to account for the results of the White Noise exposure is the known effect of stochastic resonance (Adair 2003) in biology. The stochastic resonance effect is a known signal enhancement technique (Krawiecki et al. 2000) in tele-communications and signal analysis, in which White Noise is introduced to a sub-threshold signal to elevate components of the signal above the noise threshold. This has the effect of enhancing coherent signal components above the noise-floor and producing a signal that can be recognized as an actual signal with information.
An Examination of the Contextual Interference Effect and the Errorless Learning Model during Motor Learning
Published in Journal of Motor Behavior, 2022
Hesam Ramezanzade, Esmaeel Saemi, David P. Broadbent, Jared M. Porter
While this study provided a systematic examination of the CI effect combined with the Errorless Learning model to explore optimal learning conditions, there were limitations that need to be acknowledged. In this study, participant's errors were analyzed using a continuous scale (i.e., distance from the target) rather than in a binary manner (i.e., center target hit or not), which is less common in the errorless learning literature and is more common in research examining variability of movement (e.g., Wu et al., 2014). The analysis of distance from the target provided insight in to the continuous deviations in performance from trial to trial, but the design of the study was based on the errorless learning literature (e.g., Maxwell et al., 2001). Participants threw at a target with a specific center point visible and so participants saw the binary result of their performance (i.e., whether it hit the center or not). Participants were not given any extrinsic feedback on the specific amount of movement variability on each trial (i.e., the distance from the target). This is the same procedure as studies in the errorless learning literature (e.g., Maxwell et al., 2001). So, while we analyzed performance using a continuous scale (i.e., distance from the target), we presume the error processing of the participants is similar to that of previous studies using the errorless learning approach, although we cannot be sure of this. Future research should look to provide more quantifiable insight in to processing of movement variability and ‘noise’ during an errorless learning approach. Herzfeld & Shadmehr (2014) suggest that individuals begin with large amounts of motor variability as they explore the possible motor outcomes based on the task and environment; in the case of the current study the throw action to hit the target at the different distances. Once the task is achieved, such as hitting the center target in the current study, the individual attempts to repeat the same movement and processes whether they achieve the task or not. To provide more insight in to binary error processing and continuous processing of noise and movement variability in motor learning, the differential learning (DL) approach could be a fruitful one for future research. DL is based on promoting large inter-trial fluctuations and links to the concept of stochastic-resonance (Schollhorn et al., 2006). Stochastic-resonance is a phenomenon where the presence of noise in a nonlinear system is essential for the optimal performance of the system (McDonnell et al., 2006). Accordingly, it is suggested that in future research, the potential of both errorless learning model and DL approach be considered.