Because of their nature, biomarkers for neuropsychiatric diseases were out of the reach of medical diagnostic technology until the past few decades. In recent years, the confluence of greater, affordable computer power with the need for more efficient diagnoses and treatments has increased interest in and the possibility of their discovery. This review will focus on the progress made over the past ten years regarding the search for electroencephalographic biomarkers for neuropsychiatric diseases. This includes algorithms and methods of analysis, machine learning, and quantitative electroencephalography as applied to neurodegenerative and neurodevelopmental diseases as well as traumatic brain injury and COVID-19. Our findings suggest that there is a need for consensus among quantitative electroencephalography researchers on the classification of biomarkers that most suit this field; that there is a slight disconnection between the development of increasingly sophisticated methods of analysis and what they will actually be of use for in the clinical setting; and finally, that diagnostic biomarkers are the most favored type in the field with a few caveats. The main goal of this state-of-the-art review is to provide the reader with a general panorama of the state of the art in this field.

Migraine is a common, headache disorder characterized by recurrent episodes of headache often associated with nausea, vomiting, photophobia, and phonophobia. Prior to puberty, boys and girls are equally affected. Female preponderance emerges after puberty. Migraine pathophysiology is not fully understood, and although the hormonal effect of estrogen is significant, it is not clear how hormonal phases affect brain excitability and EEG patterns in women with migraine. The objective of this research was to study the effect of migraine on the resting-state EEG activity of women during the luteal phase. This work compares electroencephalographic (EEG) absolute power in different frequency bands and scalp areas between young women who suffer from migraine and had a migraine attack within 24 h prior to EEG recording (experimental) and ten age-matched young healthy women (controls), all with normal menstrual cycles. For women with migraine, we found a significant decrease/increase in alpha power in the occipitoparietal/frontocentral area, significant decrease in beta power for all areas, significant decrease in delta power in the temporal area, and significant decrease in theta power in the frontocentral and occipitoparietal area. We concluded that women with migraine have a distinct electroencephalographic pattern during the luteal phase in comparison with control women. A possible explanation might be an intermittent rhythmic activity linked to pain.

Traumatic brain injury (TBI), resulting from external forces, is a leading cause of disability and death, often leading to cognitive deficits that affect attention, concentration, speech and language, learning and memory, reasoning, planning, and problem-solving. Given the diverse mechanisms underlying TBI symptoms, it is essential to characterize its neurophysiological and neuropsychological effects. To address this, we employed weighted coherence (WC) analysis in patients performing the Halstead–Reitan categorization task, alongside a control group of eight healthy individuals. Our findings indicate a significant decrease in WC within the theta and delta bands in the temporal regions during cognitive tasks in the TBI group compared to controls. Additionally, we observed a significant increase in WC in the beta and gamma bands in the parietal region during both rest and cognitive tasks in the TBI group, relative to the control group. Furthermore, there was a strong correlation between WC and task performance scores in the temporal regions.

Monosynaptic reflexes (MSRs) elicited by constant-intensity group I afferent stimulation exhibit marked amplitude variability, commonly attributed to stochastic presynaptic modulation and dynamic postsynaptic excitability. Here, we tested whether this variability could be attenuated using the Ott–Grebogi–Yorke (OGY) chaos–control algorithm, which stabilizes unstable periodic orbits in low-dimensional nonlinear systems. In spinalized, anesthetized cats, real-time implementation of the OGY method failed to reduce MSR amplitude variability, as quantified by the coefficient of variation, and the return map structure showed no evidence of orbit stabilization. These negative results contrast with successful applications of OGY control in physical systems, cardiac tissue, hippocampal slices, and stochastic neuronal models. We interpret this failure in the context of the intense, ongoing synaptic bombardment characteristic of dorsal horn circuitry, which likely obscures or destroys the low-dimensional geometric structure required for OGY-based control. Our findings delineate a fundamental limit to classical chaos–control algorithms in intact neural circuits and highlight the need for control strategies explicitly robust to high dimensionality and physiological noise.