Electroencephalography Phase Synchronization
Research Papers
Linking alpha oscillations, attention and inhibitory control in adult ADHD with EEG neurofeedback
Abnormal patterns of electrical oscillatory activity have been repeatedly described in adult ADHD. In particular, the alpha rhythm (8-12 Hz), known to be modulated during attention, has previously been considered as candidate biomarker for ADHD. In the present study, we asked adult ADHD patients to self-regulate their own alpha rhythm using neurofeedback (NFB), in order to examine the modulation of alpha oscillations on attentional performance and brain plasticity. Twenty-five adult ADHD patients and 22 healthy controls underwent a 64-channel EEG-recording at resting-state and during a Go/NoGo task, before and after a 30 min-NFB session designed to reduce (desynchronize) the power of the alpha rhythm. Alpha power was compared across conditions and groups, and the effects of NFB were statistically assessed by comparing behavioral and EEG measures pre-to-post NFB. Firstly, we found that relative alpha power was attenuated in our ADHD cohort compared to control subjects at baseline and across experimental conditions, suggesting a signature of cortical hyper-activation. Both groups demonstrated a significant and targeted reduction of alpha power during NFB. Interestingly, we observed a post-NFB increase in resting-state alpha (i.e. rebound) in the ADHD group, which restored alpha power towards levels of the normal population. Importantly, the degree of post-NFB alpha normalization during the Go/NoGo task correlated with individual improvements in motor inhibition (i.e. reduced commission errors) only in the ADHD group. Overall, our findings offer novel supporting evidence implicating alpha oscillations in inhibitory control, as well as their potential role in the homeostatic regulation of cortical excitatory/inhibitory balance.
View Full Paper →Cognitive Behavior Classification From Scalp EEG Signals
Electroencephalography (EEG) has become increasingly valuable outside of its traditional use in neurology. EEG is now used for neuropsychiatric diagnosis, neurological evaluation of traumatic brain injury, neurotherapy, gaming, neurofeedback, mindfulness, and cognitive enhancement training. The trend to increase the number of EEG electrodes, the development of novel analytical methods, and the availability of large data sets has created a data analysis challenge to find the "signal of interest" that conveys the most information about ongoing cognitive effort. Accordingly, we compare three common types of neural synchrony measures that are applied to EEG-power analysis, phase locking, and phase-amplitude coupling to assess which analytical measure provides the best separation between EEG signals that were recorded, while healthy subjects performed eight cognitive tasks-Hopkins Verbal Learning Test and its delayed version, Stroop Test, Symbol Digit Modality Test, Controlled Oral Word Association Test, Trail Marking Test, Digit Span Test, and Benton Visual Retention Test. We find that of the three analytical methods, phase-amplitude coupling, specifically theta (4-7 Hz)-high gamma (70-90 Hz) obtained from frontal and parietal EEG electrodes provides both the largest separation between the EEG during cognitive tasks and also the highest classification accuracy between pairs of tasks. We also find that phase-locking analysis provides the most distinct clustering of tasks based on their utilization of long-term memory. Finally, we show that phase-amplitude coupling is the least sensitive to contamination by intense jaw-clenching muscle artifact.
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