Evaluating the Electroencephalographic Signal Quality of an In-Ear Wearable Device.

Wearable in-ear electroencephalographic (EEG) devices hold significant promise for advancing brain monitoring technologies into everyday applications. However, despite the current availability of several in-ear EEG devices in the market, there remains a critical need for robust validation against established clinical-grade systems. In this study, we carried out a detailed examination of the signal performance of a mobile in-ear EEG device from Naox Technologies. Our investigation had two main goals: firstly, evaluating the hardware circuit's reliability through simulated EEG signal experiments and, secondly, conducting a thorough comparison between the in-ear EEG device and gold-standard EEG monitoring equipment. This comparison assesses correlation coefficients with recognized physiological patterns during wakefulness and sleep, including alpha rhythms, eye artifacts, slow waves, spindles, and sleep stages. Our findings support the feasibility of using this in-ear EEG device for brain activity monitoring, particularly in scenarios requiring enhanced comfort and user-friendliness in various clinical and research settings.

EEG biomarkers for the prediction of post-traumatic epilepsy - a systematic review of an emerging field.

Traumatic brain injury (TBI) is often followed by post-traumatic epilepsy (PTE), a condition often difficult to treat and leading to a substantial decline in quality of life as well as increased long-term mortality. The latent period between TBI and the emergence of spontaneous recurrent seizures provides an opportunity for pharmacological intervention to prevent epileptogenesis. Biomarkers capable of predicting PTE development are urgently needed to facilitate clinical trials of putative anti-epileptogenic drugs. EEG is a widely available and flexible diagnostic modality that plays a fundamental role in epileptology. We systematically review the advances in the field of the discovery of EEG biomarkers for the prediction of PTE in humans. Despite recent progress, the field faces several challenges including short observation periods, a focus on early post-injury monitoring, difficulties in translating findings from animal models to scalp EEG, and emerging evidence indicating the importance of assessing altered background scalp EEG activity alongside epileptiform activity using quantitative EEG methods while also considering sleep abnormalities in future studies.

Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG.

Clinical diagnosis of epilepsy depends heavily on the detection of interictal epileptiform discharges (IEDs) from scalp electroencephalographic (EEG) signals, which by purely visual means is far from straightforward. Here, we introduce a simple signal analysis procedure based on scalp EEG zero-crossing patterns which can extract the spatiotemporal structure of scalp voltage fluctuations. We analyzed simultaneous scalp and intracranial EEG recordings from patients with pharmacoresistant temporal lobe epilepsy. Our data show that a large proportion of intracranial IEDs manifest only as subtle, low-amplitude waveforms below scalp EEG background and could, therefore, not be detected visually. We found that scalp zero-crossing patterns allow detection of these intracranial IEDs on a single-trial level with millisecond temporal precision and including some mesial temporal discharges that do not propagate to the neocortex. Applied to an independent dataset, our method discriminated accurately between patients with epilepsy and normal subjects, confirming its practical applicability.

Periodic limb movements in sleep are followed by increases in EEG activity, blood pressure, and heart rate during sleep.

PURPOSE: Periodic limb movements in sleep (PLMS) are related to arousal, sympathetic activation, and increases in blood pressure (BP), but whether they are part of the arousal process or causative of it is unclear. Our objective was to assess the temporal distribution of arousal-related measures around PLMS. METHODS: Polysomnographic recordings of six patients with restless legs syndrome were analyzed. We analyzed 15 PLMS, plus three 5-s epochs before and after each movement, for every patient. Mean values per epoch of blood pressure (BP), heart rate (HR), and electroencephalographic (EEG) power were calculated. For each patient, six 5-s epochs of undisturbed sleep were analyzed as controls. RESULTS: Alpha + beta EEG power, systolic BP, and HR were significantly increased following PLMS. The EEG power and HR increases were noticed in the first epoch after PLMS, whereas that of systolic BP was observed in the second and third epochs following a PLMS. No significant changes occurred in the epochs of undisturbed sleep. CONCLUSIONS: The results suggest that PLMS are followed by arousal-related nervous system events. Given the high frequency of PLMS throughout the night, they could be a potential risk factor for nocturnal arrhythmias and hypertension, in addition to causing sleep deprivation.

Automated detection of high-frequency oscillations in electrophysiological signals: Methodological advances.

In recent years, new recording technologies have advanced such that oscillations of neuronal networks can be identified from simultaneous, multisite recordings at high temporal and spatial resolutions. However, because of the deluge of multichannel data generated by these experiments, achieving the full potential of parallel neuronal recordings also depends on the development of new mathematical methods capable of extracting meaningful information related to time, frequency and space. In this review, we aim to bridge this gap by focusing on the new analysis tools developed for the automated detection of high-frequency oscillations (HFOs, >40Hz) in local field potentials. For this, we provide a revision of different aspects associated with physiological and pathological HFOs as well as the several stages involved in their automatic detection including preprocessing, selection, rejection and analysis through time-frequency processes. Beyond basic research, the automatic detection of HFOs would greatly assist diagnosis of epilepsy disorders based on the recognition of these typical pathological patterns in the electroencephalogram (EEG). Also, we emphasize how these HFO detection methods can be applied and the properties that might be inferred from neuronal signals, indicating potential future directions.

Interval analysis of interictal EEG: pathology of the alpha rhythm in focal epilepsy.

The contemporary use of interictal scalp electroencephalography (EEG) in the context of focal epilepsy workup relies on the visual identification of interictal epileptiform discharges. The high-specificity performance of this marker comes, however, at a cost of only moderate sensitivity. Zero-crossing interval analysis is an alternative to Fourier analysis for the assessment of the rhythmic component of EEG signals. We applied this method to standard EEG recordings of 78 patients divided into 4 subgroups: temporal lobe epilepsy (TLE), frontal lobe epilepsy (FLE), psychogenic nonepileptic seizures (PNES) and nonepileptic patients with headache. Interval-analysis based markers were capable of effectively discriminating patients with epilepsy from those in control subgroups (AUC~0.8) with diagnostic sensitivity potentially exceeding that of visual analysis. The identified putative epilepsy-specific markers were sensitive to the properties of the alpha rhythm and displayed weak or non-significant dependences on the number of antiepileptic drugs (AEDs) taken by the patients. Significant AED-related effects were concentrated in the theta interval range and an associated marker allowed for identification of patients on AED polytherapy (AUC~0.9). Interval analysis may thus, in perspective, increase the diagnostic yield of interictal scalp EEG. Our findings point to the possible existence of alpha rhythm abnormalities in patients with epilepsy.