Quantitative and Qualitative EEG as a Prediction Tool for Outcome and Complications in Acute Stroke Patients.

Currently, the relevance of EEG measurements in acute stroke patients is considered low in clinical practice. However, recent studies on the predictive value of EEG measurements after stroke for various outcomes may increase the role of EEG in patients with stroke. We aimed to review the current literature on the utility of EEG measurements after stroke as a tool to predict outcome and complications, focusing on studies in which the EEG measurement was performed in the acute phase after the event and in which long-term outcome measures were reported. In our literature review, we identified 4 different outcome measures (functional outcome, mortality, development of post-stroke cognitive decline, and development of post-stroke epilepsy) where studies on the utility of acute EEG measurements exist. There is a large body of evidence for the prediction of functional outcome, in which a multitude of associated quantitative and qualitative EEG parameters are described. In contrast, only few studies focus on mortality as outcome parameter. We found studies of high methodical quality on the prediction of post-stroke cognitive decline, though the number of patients in these studies often was small. The role of EEG as a prediction tool for seizures and epilepsy after stroke could increase after a recently published study, especially if its result can be incorporated into already existing post-stroke epilepsy prediction tools. In summary, EEG is useful for the prediction of functional outcome, mortality, development of post-stroke cognitive decline and epilepsy, even though there is a discrepancy between the large amount of studies on EEG in acute stroke patients and its underuse in clinical practice.

Predictive value of acute EEG measurements for seizures and epilepsy after stroke using a dry cap electrode EEG system - Study design and proof of concept.

The development of seizures is a common complication in patients with stroke. A recent study suggests that electroencephalography (EEG) could be useful to predict the occurrence of seizures after stroke. However, EEG is not routinely performed in the follow-up after stroke. We present the design and proof of concept of a large prospective cohort study in which we seek to evaluate the predictive usefulness of a single dry cap EEG measurement in the acute phase after stroke or transient ischemic attack (TIA) with respect to the development of poststroke epilepsy (PSE). In patients with ischemic stroke (IS), TIA, or intracerebral hemorrhage (ICH), an acute EEG measurement using a dry cap electrode EEG (dEEG) system is performed within 7 days of symptom onset. Electroencephalography data will be analyzed quantitatively and qualitatively looking for background asymmetry, different band power ratios, and epileptiform activity. The development of seizures will be assessed repeatedly during the first 2 years after stroke or TIA using a validated questionnaire. Results from the pilot phase of 11 patients showed that the dry cap EEG measurements in patients with acute stroke and TIA are feasible. In comparison with conventional EEG, a higher proportion of noise and artifacts was detected. This article is part of the Special Issue "Seizure & Stroke".

A Gaze Independent Brain-Computer Interface Based on Visual Stimulation through Closed Eyelids.

A classical brain-computer interface (BCI) based on visual event-related potentials (ERPs) is of limited application value for paralyzed patients with severe oculomotor impairments. In this study, we introduce a novel gaze independent BCI paradigm that can be potentially used for such end-users because visual stimuli are administered on closed eyelids. The paradigm involved verbally presented questions with 3 possible answers. Online BCI experiments were conducted with twelve healthy subjects, where they selected one option by attending to one of three different visual stimuli. It was confirmed that typical cognitive ERPs can be evidently modulated by the attention of a target stimulus in eyes-closed and gaze independent condition, and further classified with high accuracy during online operation (74.58% ± 17.85 s.d.; chance level 33.33%), demonstrating the effectiveness of the proposed novel visual ERP paradigm. Also, stimulus-specific eye movements observed during stimulation were verified as reflex responses to light stimuli, and they did not contribute to classification. To the best of our knowledge, this study is the first to show the possibility of using a gaze independent visual ERP paradigm in an eyes-closed condition, thereby providing another communication option for severely locked-in patients suffering from complex ocular dysfunctions.