Also, the precise spectral location of the peak frequency for the alpha (8–12 Hz) range is variable across individuals, and the location of this peak is a meaningful parameter that has been

correlated with development (Cragg et al. 2011) and cognitive performance (Angelakis et al. 2004). Engagement with an individual’s unique spectral EEG fingerprint is not possible with technologies that Inhibitors,research,lifescience,medical rely on standard broadband EEG frequency ranges. HIRREM and EEG artifact or noise Artifact identification and rejection are thematic to the field of EEG. EEG artifacts may include a variety of discrete phenomena including abnormalities of the EEG tracing which are due not to neural oscillation but rather to scalp muscular contraction, eyeblinking, or head or sensor movement. For the practice of EEG operant conditioning, the identification of EEG artifact is mission-critical, click here because the presentation of reward or inhibit signals in response to peripheral Inhibitors,research,lifescience,medical muscular contractions (for example), rather than neuronal oscillations, is subversive to the purpose and basis of the enterprise. (Likewise, artifact identification is critical for medical EEG especially insofar as definitive diagnosis depends on accurate characterization of EEG waveforms which are abnormal

but may manifest inconsistently.) Because HIRREM technology does not aim Inhibitors,research,lifescience,medical to consciously teach the individual through signals of reward or inhibition, we postulate that there is little if any jeopardy associated with providing auditory signals which are informed by nonneural sources and are therefore “meaningless.” (Nor does HIRREM aim to Inhibitors,research,lifescience,medical diagnose disease.)

Rather we infer that the brain responds to epochs of HIRREM sounds generated from grossly noisy EEG artifact in the way Inhibitors,research,lifescience,medical that it would respond to grossly noisy sounds. Furthermore, artifact-associated data will tend to be distributed symmetrically, and because HIRREM algorithms are based on the relationship of activity between homologous brain regions, artifactual signals will tend to cancel one another out in the algorithmic equation. We also hypothesize that, paradoxically, a possible mechanism for benefit of HIRREM could be the engagement between HIRREM and what is generally considered background noise or randomness in the EEG. The core technical aim of HIRREM is to resonate with dynamically changing second dominant frequencies in the spectral EEG. Variations of amplitudes in these frequencies are typically characterized in stochastic terms. That is, the energies of interest to HIRREM are in the category of apparently random fluctuations in the EEG, or noise. Variations in system noise levels can change the probability that a weak periodic signal will cross a threshold for sensory processing. The presence of an optimal noise level in a system can improve detection of a weak periodic signal, by boosting the signal sufficiently to cross the output threshold.