The NMT Scalp EEG Dataset: An Open-Source Annotated Dataset of Healthy and Pathological EEG Recordings for Predictive Modeling

Electroencephalogram (EEG) is widely used for the diagnosis of neurological conditions like epilepsy, neurodegenerative illnesses and sleep related disorders. Proper interpretation of EEG recordings requires the expertise of trained neurologists, a resource which is scarce in the developing world. Neurologists spend a significant portion of their time sifting through EEG recordings looking for abnormalities. Most recordings turn out to be completely normal, owing to the low yield of EEG tests. To minimize such wastage of time and effort, automatic algorithms could be used to provide pre-diagnostic screening to separate normal from abnormal EEG. Data driven machine learning offers a way forward however, design and verification of modern machine learning algorithms require properly curated labeled datasets. To avoid bias, deep learning based methods must be trained on large datasets from diverse sources. This work presents a new open-source dataset, named the NMT Scalp EEG Dataset, consisting of 2,417 recordings from unique participants spanning almost 625 h. Each recording is labeled as normal or abnormal by a team of qualified neurologists. Demographic information such as gender and age of the patient are also included. Our dataset focuses on the South Asian population. Several existing state-of-the-art deep learning architectures developed for pre-diagnostic screening of EEG are implemented and evaluated on the NMT, and referenced against baseline performance on the well-known Temple University Hospital EEG Abnormal Corpus. Generalization of deep learning based architectures across the NMT and the reference datasets is also investigated. The NMT dataset is being released to increase the diversity of EEG datasets and to overcome the scarcity of accurately annotated publicly available datasets for EEG research.

Ictal Blinking: Reappraisal of the Lateralization and Localization Value in Focal Seizures

Introduction. Although ictal blinking is significantly more frequent in generalized epilepsy, it has been reported as a rare but useful lateralizing sign in focal seizures when it is not associated with facial clonic twitching. This study aimed to raise awareness of eye blinking as a semiological lateralizing sign. Method. Our database over an 11-year period reviewed retrospectively to assess patients who had ictal blinking associated with focal seizures. Results. Among 632 patients, 14 (2.2%), who had 3 to 13 (7 ± 3) seizures during video-EEG monitoring, were included. Twenty-five percent of all 92 seizures displayed ictal blinking and each patient had one to five seizures with ictal blinking. Ictal blinking was unilateral in 17%, asymmetrical in 22% and symmetrical in 61%. The blinking appeared with a mean latency of 6.3 s (range 0-39) after the clinical seizure-onset, localized most often to fronto-temporal, then in frontal or occipital regions. Blinking was ipsilateral to ictal scalp EEG lateralization side in 83% (5/6) of the patients with unilateral/asymmetrical blinking. The exact lateralization and localization of ictal activity could not have been determined via EEG in most of the patients with symmetrical blinking, remarkably. Conclusions. Unilateral/asymmetrical blinking is one of the early components of the seizures and appears as a useful lateralizing sign, often associated with fronto-temporal seizure-onset. Symmetrical blinking, on the other hand, did not seem to be valuable in lateralization and localization of focal seizures. Future studies using invasive recordings and periocular electrodes are needed to evaluate the value of blinking in lateralization and localization.

Seizure Prediction With HIVE-CODAs: The Hierarchical Vote Collective of Domain Adaptation Methods

Epileptic seizure prediction is one of the most used therapeutic adjuvant strategies for drug-resistant epilepsy. Conventional methods are usually trained and tested on the same patient due to the interindividual variability. However, the challenging problem of the domain shift between different subjects remains unsolved, resulting in low prevalence of clinical application. In this study, a generic model based on the domain adaptation (DA) technique is proposed to alleviate such problems. Ensemble learning is employed by developing a hierarchical vote collective of seven DA modules over multi-modality data, such that the predictive performance is improved by training multiple models. Moreover, to increase the feasibility of its implementation, this study mimics the data distribution of clinical sampling and tests the model under this simulated realistic condition. Based on the performance of seven subnetworks, the applicability of each DA algorithm for seizure prediction is evaluated, which is the first study that provides the assessment. Experimental results on both intracranial and scalp EEG databases demonstrate that this method can reduce the domain gap effectively compared with previous studies.

The hippocampus as the switchboard between perception and memory

Adaptive memory recall requires a rapid and flexible switch from external perceptual reminders to internal mnemonic representations. However, owing to the limited temporal or spatial resolution of brain imaging modalities used in isolation, the hippocampal–cortical dynamics supporting this process remain unknown. We thus employed an object-scene cued recall paradigm across two studies, including intracranial electroencephalography (iEEG) and high-density scalp EEG. First, a sustained increase in hippocampal high gamma power (55 to 110 Hz) emerged 500 ms after cue onset and distinguished successful vs. unsuccessful recall. This increase in gamma power for successful recall was followed by a decrease in hippocampal alpha power (8 to 12 Hz). Intriguingly, the hippocampal gamma power increase marked the moment at which extrahippocampal activation patterns shifted from perceptual cue toward mnemonic target representations. In parallel, source-localized EEG alpha power revealed that the recall signal progresses from hippocampus to posterior parietal cortex and then to medial prefrontal cortex. Together, these results identify the hippocampus as the switchboard between perception and memory and elucidate the ensuing hippocampal–cortical dynamics supporting the recall process.

Mobile BCI dataset of scalp- and ear-EEGs with ERP and SSVEP paradigms while standing, walking, and running

We present a mobile dataset obtained from electroencephalography (EEG) of the scalp and around the ear as well as from locomotion sensors by 24 participants moving at four different speeds while performing two brain-computer interface (BCI) tasks. The data were collected from 32-channel scalp-EEG, 14-channel ear-EEG, 4-channel electrooculography, and 9-channel inertial measurement units placed at the forehead, left ankle, and right ankle. The recording conditions were as follows: standing, slow walking, fast walking, and slight running at speeds of 0, 0.8, 1.6, and 2.0 m/s, respectively. For each speed, two different BCI paradigms, event-related potential and steady-state visual evoked potential, were recorded. To evaluate the signal quality, scalp- and ear-EEG data were qualitatively and quantitatively validated during each speed. We believe that the dataset will facilitate BCIs in diverse mobile environments to analyze brain activities and evaluate the performance quantitatively for expanding the use of practical BCIs.

Reflection of the Ictal Electrocorticographic Discharges Confined to the Medial Temporal Lobe to the Scalp-Recorded Electroencephalogram

Objective: Previous reports on the simultaneous recording of electroencephalography (EEG) and electrocorticography (ECoG) have demonstrated that, in patients with temporal lobe epilepsy (TLE), ictal ECoG discharges with an amplitude as high as 1000 μV originating from the medial temporal lobe could not be recorded on EEG. In contrast, ictal EEG discharges were recorded after ictal ECoG discharges propagated to the lateral temporal lobe. Here, we report a case of TLE in which the ictal EEG discharges, corresponding to ictal ECoG discharges confined to the medial temporal lobe, were recorded. Case report: In the present case, ictal EEG discharges were hardly recognized when the amplitude of the ECoG discharges was less than 1500 μV. During the evolution and burst suppression phase, corresponding to highly synchronized ECoG discharges with amplitudes greater than 1500 to 2000 μV, rhythmic negative waves with the same frequency were clearly recorded both on the lateral temporal lobe and scalp. The amplitude of the lateral temporal ECoG was approximately one-tenth of that of the medial temporal ECoG. The amplitude of the scalp EEG was approximately one-tenth of that of the lateral temporal ECoG. Conclusions: Highly synchronized ictal ECoG discharges with high amplitude of greater than 1500 to 2000 μV in the medial temporal lobe could be recorded on the scalp as ictal EEG discharges via volume conduction.