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Brainwave Optimization

Lan Ma, James W Minett, Thierry Blu, William S-Y Wang
Biometrics is a growing field, which permits identification of individuals by means of unique physical features. Electroencephalography (EEG)-based biometrics utilizes the small intra-personal differences and large inter-personal differences between individuals' brainwave patterns. In the past, such methods have used features derived from manually-designed procedures for this purpose. Another possibility is to use convolutional neural networks (CNN) to automatically extract an individual's best and most unique neural features and conduct classification, using EEG data derived from both Resting State with Open Eyes (REO) and Resting State with Closed Eyes (REC)...
August 2015: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Wei-Yen Hsu
An EEG classifier is proposed for application in the analysis of motor imagery (MI) EEG data from a brain-computer interface (BCI) competition in this study. Applying subject-action-related brainwave data acquired from the sensorimotor cortices, the system primarily consists of artifact and background removal, feature extraction, feature selection and classification. In addition to background noise, the electrooculographic (EOG) artifacts are also automatically removed to further improve the analysis of EEG signals...
December 2015: International Journal of Neural Systems
A H Jahidin, M S A Megat Ali, M N Taib, N Md Tahir, I M Yassin, S Lias
This paper elaborates on the novel intelligence assessment method using the brainwave sub-band power ratio features. The study focuses only on the left hemisphere brainwave in its relaxed state. Distinct intelligence quotient groups have been established earlier from the score of the Raven Progressive Matrices. Sub-band power ratios are calculated from energy spectral density of theta, alpha and beta frequency bands. Synthetic data have been generated to increase dataset from 50 to 120. The features are used as input to the artificial neural network...
April 2014: Computer Methods and Programs in Biomedicine
Jae Won Bang, Jong-Suk Choi, Kang Ryoung Park
Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have been used in various applications, including human-computer interfaces, diagnosis of brain diseases, and measurement of cognitive status. However, EEG signals can be contaminated with noise caused by user's head movements. Therefore, we propose a new method that combines an EEG acquisition device and a frontal viewing camera to isolate and exclude the sections of EEG data containing these noises. This method is novel in the following three ways...
2013: Sensors
Lee Gerdes, Peter Gerdes, Sung W Lee, Charles H Tegeler
Disturbances of neural oscillation patterns have been reported with many disease states. We introduce methodology for HIRREM™ (high-resolution, relational, resonance-based electroencephalic mirroring), also known as Brainwave Optimization™, a noninvasive technology to facilitate relaxation and auto-calibration of neural oscillations. HIRREM is a precision-guided technology for allostatic therapeutics, intended to help the brain calibrate its own functional set points to optimize fitness. HIRREM technology collects electroencephalic data through two-channel recordings and delivers a series of audible musical tones in near real time...
March 2013: Brain and Behavior
Jonas Duun-Henriksen, Rasmus E Madsen, Line S Remvig, Carsten E Thomsen, Helge B D Sorensen, Troels W Kjaer
Automatic detections of paroxysms in patients with childhood absence epilepsy have been neglected for several years. We acquire reliable detections using only a single-channel brainwave monitor, allowing for unobtrusive monitoring of antiepileptic drug effects. Ultimately we seek to obtain optimal long-term prognoses, balancing antiepileptic effects and side effects. The electroencephalographic appearance of paroxysms in childhood absence epilepsy is fairly homogeneous, making it feasible to develop patient-independent automatic detection...
May 2012: Pediatric Neurology
Michael Bewernitz, Hartmut Derendorf
Pharmacokinetics and pharmacodynamics can provide a useful modeling framework for predicting drug activity and can serve as a basis for dose optimization. Determining a suitable biomarker or surrogate measure of drug effect for pharmacodynamic models can be challenging. The electroencephalograph is a widely-available and non-invasive tool for recording brainwave activity simultaneously from multiple brain regions. Certain drug classes (such as drugs that act on the central nervous system) also generate a reproducible electroencephalogram (EEG) effect...
March 2012: International Journal of Clinical Pharmacology and Therapeutics
Tomas Ros, Merrick J Moseley, Philip A Bloom, Larry Benjamin, Lesley A Parkinson, John H Gruzelier
BACKGROUND: By enabling individuals to self-regulate their brainwave activity in the field of optimal performance in healthy individuals, neurofeedback has been found to improve cognitive and artistic performance. Here we assessed whether two distinct EEG neurofeedback protocols could develop surgical skill, given the important role this skill plays in medicine. RESULTS: National Health Service trainee ophthalmic microsurgeons (N = 20) were randomly assigned to either Sensory Motor Rhythm-Theta (SMR) or Alpha-Theta (AT) groups, a randomized subset of which were also part of a wait-list 'no-treatment' control group (N = 8)...
2009: BMC Neuroscience
Udo Will, Eric Berg
As known, different brainwave frequencies show synchronies related to different perceptual, motor or cognitive states. Brainwaves have also been shown to synchronize with external stimuli with repetition rates of ca. 10-40 Hz. However, not much is known about responses to periodic auditory stimuli with periodicities found in human rhythmic behavior (i.e. 0.5-5 Hz). In an EEG study we compared responses to periodic stimulations (drum sounds and clicks with repetition rates of 1-8 Hz), silence, and random noise...
August 31, 2007: Neuroscience Letters
S Zhou, C Wang, J Wei, S Wu
Fuzzy c-mean algorithm was applied to segment spatiotemporal patterns of brainwave into microstates and memberships. The optimal clustering number was estimated with both the trends of objective function and the eigenvalue number of microstates. Comparable spatial patterns may occur at different temporal moments in consideration of fuzzy index that is beyond the limit of serial processing. Those techniques were illustrated with multichannel event-related potentials recorded from 9 subjects during Stroop test...
1999: Brain Topography
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