#Stages of sleep eeg how to#

An idealized sleep вЂњhypnogramвЂќ example is given below. Sleep states happen usually in clearly defined temporal cycles about every 1 to 2 hours. Furthermore, the movements indicate where we can find artifactual data in our recordings we might want to exclude for more focused analyses. These arousals and movement times do not necessarily reflect the Wake state, but give us information about sleep as well. Often we have movement arousals as well as longer times with movement during sleep. A more coarse classification consists of Wake, REM and non-REM, where non-REM combines Stage 1, Stage 2 and SWS sleep. In the more recent terminology Stage 3 and Stage 4 are combined as one stage and called slow-wave sleep (SWS). Sleep has well defined brain states which are called Wake, Stage 1, Stage 2, Stage 3, Stage 4 and rapid-eye-movement (REM) sleep. Here we can gain some skills to explore cross-modality relations in recordings that are less well defined, e.g., task or resting state recordings, or parallel recordings that expand the interpretability by adding features of other or related modalities (e.g., motion sensors, MEG, fMRI).

#Stages of sleep eeg how to#

Thus using sleep data allows us to safely explore how to identify changes in brain state in a well studied example. Finally, most of the activity which we usually consider as artifacts in our task-related EEG recordings (e.g., eye movements, muscle and heart activity) occur here in a systematic manner and are considered important features of a sleep state rather than an artifact. We have some good understanding about the physiological relationships between the events and what is happening to the different body parts during sleep. This is why sleep recordings span multiple modalities, each with clearly defined changes in the activity that are relatively easy to predict.

They have to be identified and marked by using the combination of the different modalities. Background Multi-modal sleep recordingsДiscrete events and continuous shifts in activity during sleep are not easily observed in one modality by itself. This tutorial assumes that the steps of preprocessing are already clear for the reader. Along the way you will learn a bit about the structure of sleep. The methods used for analyzing sleep might also help you in characterizing drowsiness during a task, detect closed eyes or eye movements, detect spontaneous events in EEG (such as epileptic spikes) and in general how to include other modalities relevant to your task. Sleep is the most standardized and well-analyzed brain state to date. We will be using sleep recordings as an example for multimodal data and will form a picture of what is happening in the brain during sleep. You will learn how combining the modalities can give a better understanding of the brain states and the switching between them. In this tutorial you will explore and combine continuous EEG with multiple recordings from other modalities, such as muscles (EMG), eyes (EOG) and the heart (ECG). Tutorial sleep edf eeg emg ecg artifacts continuous Extracting the brain state and events from continuous sleep EEG Introduction