China
Africa
Explore chapters and articles related to this topic
The biological and evolutionary foundations of sleep and dreams
Published in Frederick L. Coolidge, Ernest Hartmann, Dream Interpretation as a Psychotherapeutic Technique, 2018
Frederick L. Coolidge, Ernest Hartmann
There is a second waking brain wave called the beta rhythm (13 to 30 Hz), which was also first described by Berger (1930). Beta consists of low voltage waves, rapidly occurring, and appearing irregular. When people are awake, they typically alternate between alpha and beta, and beta occurs all over the scalp, even over the occipital lobes when alpha is blocked. It is believed that alpha and beta waves (and all other EEG waves) are created by large clusters of brain cells in the cerebral cortex (the upper layers of the brain) as a result of postsynaptic activity. It is also thought that the voltage of a wave will increase if the synaptic activity of the cells is synchronized. Thus, alpha is believed to be the result of synchronized neuronal activity while beta is the product of desynchronized activity.
ECoG-Based BCIs
Published in Chang S. Nam, Anton Nijholt, Fabien Lotte, Brain–Computer Interfaces Handbook, 2018
Over the past several years, human and animal neurophysiologists have begun to increasingly explore ECoG to explain neurophysiological correlates of disease. Intraoperative studies during deep brain stimulation (DBS) electrode implantation surgeries (during which the patients remain awake) provide a particularly opportune window for these studies. Acute intraoperative ECoG strips can be implanted during these surgeries to study thalamocortical or basal ganglia–cortical pathways of disease. Many studies in the literature point to pathologically high beta band activity in the basal ganglia–cortical network in Parkinson’s disease (PD) (Bronte-Stewart et al. 2009; Brown et al. 2001; Levy et al. 2002). Moreover, the amplitude of the beta rhythm correlates with clinical measures of symptom severity in PD (Bronte-Stewart et al. 2009; Brown et al. 2001; Levy et al. 2002). In a similar intraoperative study of essential tremor (ET), which consists mostly of slow tremors (4–8 Hz) of the upper extremities, Air et al. (2012) reported high coherence with the primary motor cortex ECoG activity and an accelerometer placed on the tremor dominated hands of patients. Studying the neural correlates of neurological disorders not only contributes to our understanding of the pathophysiologies, but may also allow us to develop better treatment strategies.
Solving The Mystery Of The Nerve Impulse
Published in Andrew P. Wickens, A History of the Brain, 2014
Remarkably, Berger found the cerebral cortex did not exhibit a random mass of neural ‘noise’ as might be expected. Instead, it showed a regular rhythm, which in a resting conscious person with eyes closed, was around ten cycles (‘beats’) per second. Berger called this the alpha rhythm. This rhythm became faster (12 cycles per second or more) when the person was aroused (the beta rhythm). The apparatus had significant clinical potential, but for reasons that are unclear, the German medical authorities reacted to Berger’s findings with indifference and hostility. According to some biographers, this was because Berger was opposed to Nazi rule, which led to his removal from the Swiss University of Jena in the late 1930s – an event likely to have contributed to his suicide by hanging in 1941. However, some commentators have recently given alternative reasons.9 Whatever the truth of the matter, one thing is clear: the EEG only became widely known when Adrian, after reading about Berger’s research in 1932, demonstrated it to the Physiological Society in Cambridge in 1934. Following this, the EGG quickly became used as an important tool for diagnosing epilepsy, and an experimental means for measuring brain activity.
Electroencephalographic changes using virtual reality program: technical note
Published in Neurological Research, 2018
Síria Monyelle Silva de Oliveira, Candice Simões Pimenta de Medeiros, Thaiana Barbosa Ferreira Pacheco, Nathalia Priscilla Oliveira Silva Bessa, Fernanda Gabrielle Mendonça Silva, Nathália Stéphany Araújo Tavares, Isabelle Ananda Oliveira Rego, Tania Fernandes Campos, Fabrícia Azevedo da Costa Cavalcanti
Each electrical signal is registered synchronously in terms of frequency and is known as brain rhythms: delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (Above 30 Hz) [2]. The delta rhythm is related to a coma state, deep sleep, newborns, and some brain dysfunction. Theta rhythm is generally observed in states of deep meditation, relaxation, and automated activities. Alpha rhythm is associated with mild states of alertness, meditation and alertness with closed eyes. The beta rhythm is observed in alert states, mental effort, decision-making and external information processing. Finally, gamma rhythm is associated with information processing, voluntary movements, and attention control [1,2].
The simultaneous changes in motor performance and EEG patterns in beta band during learning dart throwing skill in dominant and non-dominant hand
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Yaser Khanjari, Elahe Arabameri, Mehdi Shahbazi, Shahzad Tahmasebi, Fariba Bahrami, Ali Mobaien
Another result of this study was the existence of relatively constant changes in EEG pattern and skill performance simultaneously in acquisition and retention after training interventions in dominant and non-dominant subjects. Although the research background on the relationship between improved motor skills and the emergence of a new cortical map is limited, there is evidence to suggest simultaneous changes in movement performance and cortical mapping (Eyre et al. 2000). Aumann and Prut (2015) showed that beta-rhythm oscillations increase cortical-muscular communication and thereby help maintain newer accuracy and representations of muscle patterns in the cortex (Aumann and Prut 2015). Previous research has reported that beta wave oscillations are associated with cognitive reorganization and memory consolidation associated with learning a motor task (Engel and Fries 2010). Also, other studies have shown that as exercise progresses, skillful patterns appear that are coded in the motor cortex (Hess 2002). On the other hand, the learning process is a neuro-physiological mechanism that is constantly created by the reorganization of motor representations in the motor cortex, and it is believed that each specific motor map is a motor evolution (Monfils et al. 2005). Therefore, in the present study, one of the possible reasons for cortical map changes could be the deformation of neural connections and re-coding during dart skill learning, which at the same time changed the muscle pattern of the skill and improved performance. In fact, with increasing practice and skill learning, the pattern of cortical mapping has changed and a new pattern of brain activity has been created that has led to the use of a skillful pattern of movement and reduced error.
The Post-Movement Beta Rebound and Motor-Related Mu Suppression in Children
Published in Journal of Motor Behavior, 2020
Junyi Hao, Wenfeng Feng, Lingli Zhang, Yu Liao
For mu rhythm, the mu power decreased significantly during movement, and it returned to baseline level when the movement terminated for both children and adults. These results are consistent with the idea that mu rhythm has adult-like characteristics from an early age of life (Liao et al., 2015; Marshall et al., 2011; Southgate et al., 2009). However, the beta rhythm showed different dynamics between children and adults. The movement-induced beta ERD was observed in both children and adults, but the primary difference in the beta dynamics was the presence of PMBR only in adults. Since the age range in the current study is relatively wide, one can argue that the grand average of the whole sample may under estimated the older children’s PMBR strength. Benefit from the improved signal-to-noise ratio of ICA generated motor component, we were able to examine ERSP for each individual, for all children older than seven, only one girl age at 8 years and11month in our tested sample showed observable PMBR. Thus, it is unlikely the grand averaged data in the children’s group washed out the older children’s PMBR. This is the first EEG evidence to find that PMBR may not yet have developed after a goal-directed arm movement in children between the ages of five to nine. This is consistent with a recent study that used an MEG approach to investigate the PMBR of children (4–6 years), adolescents (11–13 years) and adults (Gaetz et al., 2010). Gaetz et al. (2010) found that at the end of index finger movement, the PMBR was absent in children, while it came online gradually in adolescents (Gaetz et al., 2010). This result is also consistent with an MEG study that measured PMBR from 94 children and adolescents (9–15 years old; Trevarrow et al., 2019), where they found that PMBR became significantly stronger during the transition from childhood to adolescence.