Electrical Brain Stimulation to Treat Neurological Disorders
Bahman Zohuri, Patrick J. McDaniel in Electrical Brain Stimulation for the Treatment of Neurological Disorders, 2019
Electroencephalography (EEG) is an electrophysiological monitoring method to record electrical activity of the brain. It is typically noninvasive, with the electrodes placed along the scalp, although invasive electrodes are sometimes used such as in electrocorticography. EEG measures voltage fluctuations resulting from ionic current within the neurons of the brain. In clinical contexts, EEG refers to the recording of the brain’s spontaneous electrical activity over a period of time, as recorded from multiple electrodes placed on the scalp. Diagnostic applications generally focus either on event-related potentials or on the spectral content of EEG. The former investigates potential fluctuations time locked to an event like stimulus onset or button press. The latter analyses the type of neural oscillations (popularly called “brain waves”) that can be observed in EEG signals in the frequency domain.
Neuroimaging of sleep and depression
S.R. Pandi-Perumal, Meera Narasimhan, Milton Kramer in Sleep and Psychosomatic Medicine, 2017
Future research should seek to clarify these findings through improved techniques and replication and further our understanding of sleep in both healthy and pathologic populations. Processes like sleep-related memory consolidation and dreaming have already been explored with neuroimaging, but still remain poorly understood, leaving much room for future research. Our understanding of neural oscillations during sleep also has much room for new discoveries, particularly in terms of understanding the functional purpose of phasic events. Lastly, sleep disturbances are widely known to be related to many psychiatric and neurologic disorders, but the exact relationship is unclear. Are sleep problems predictors or symptoms of these? How are changes in brain activation and/or morphology involved in this relationship? Neuroimaging provides a powerful means for investigating all of these questions, and much remains to be uncovered about the sleeping brain.
Brain–Computer Interface
Chang S. Nam, Anton Nijholt, Fabien Lotte in Brain–Computer Interfaces Handbook, 2018
Neural oscillations that are observed in EEG signals are popularly called “brainwaves,” reflecting different aspects when they occur in different locations in the brain (Table 1.1). These brainwaves are identified by frequency (in hertz or cycles per second) and amplitude in the range of microvolts (μV or 1/1,000,000 of a volt). Each brainwave has its own set of characteristics representing a specific level of brain activity and mental states (Mühl et al. 2014). For example, Delta brainwaves reflect slow, loud, and functional mental states that prevail during the late sleep (Steriade et al. 1993), while the power decrease at the alpha band correlates to the presence of mental imagery (Pfurtscheller & Lopes da Silva 1999).
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
Concurrent GVS and neuroimaging in PD could further advance the understanding of potential neural mechanisms, as well as assisting in developing advanced stimulation protocols, such as those for personalized stimulation. A technical challenge of concurrent EEG and MEG is the stimulation-induced artifacts in the recordings, which can be several orders of magnitude larger in amplitude than the actual brain signals. Although this can be easily resolved by applying digital filters if the stimulation frequency is out of the range of the neural oscillations of interest, it is common that the stimulation frequencies lie within the range of human brain oscillations (<50 Hz). While several signal processing methods such as adaptive filtering and joint blind source separation approaches have been proposed to tackle the challenge [121], this is still an area of active exploration which can aid in a better understanding of the stimulation effects.
Alpha synchronisation of acoustic responses in active listening is indicative of native language listening experience
Published in International Journal of Audiology, 2022
Alyssa Dyball, Nan Xu Rattanasone, Ronny Ibrahim, Mridula Sharma
One way to analyse induced activities is by using Time-frequency analyses (TFA). TFA works by analysing the electrophysiological data into its component frequencies such as alpha, theta, beta and gamma components across time, to isolate time points at which the neuronal oscillatory activity is strongest (Klimesch et al. 2000). Current understanding of neural oscillations suggests that neural activity within specific wavelengths is reflective of different cognitive processes. Alpha for example is most closely linked to directed attention (Klimesch et al. 1990). The induced activity can also be described as being either relatively increased (event-related synchronised) or decreased (event-related desynchronised) (Oostenveld et al. 2011). For example, increased synchronisation in the alpha-band is thought to reflect increased inhibition while desynchronisation in the alpha-band is reportedly reflective of focussed attention (Klimesch et al., 2012). Thus, TFA can assist in providing information on how the brain orchestrates the processing of linguistically relevant acoustic information.
Atypical neural oscillations in response to speech in infants and children with speech and language impairments: a systematic review
Published in Hearing, Balance and Communication, 2022
Caroline Nallet, Judit Gervain
In the studies we identified, two main approaches have been taken. Some studies explored the correlation between resting state oscillatory activity in the frontal and prefrontal areas at an early age and later language outcomes [25–27]. Other studies investigated actual oscillatory entrainment in the auditory cortex to auditory stimuli, both linguistic [28–31] and non-linguistic [32–36], in children with speech-language impairments [23,28–32,34] or at risk for such impairments [33,35,36]. Our review focuses on the second approach, as we are specifically interested in neural oscillations in responses to speech and auditory stimuli.
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