sktime-neuro is a package for machine learning with neurological data. sktime-neuro originated in a Google Summer of Code project in 2021. sktime-neuro is built on top of sktime (https://github.com/alan-turing-institute/sktime).
From October 2022 we will focus on classifying EEG data as part of work package 3 (WP3) of EPSRC grant EP/W030756/1.
EEG records electrical activity in the brain using a series electrodes placed on the scalp. EEG equipment is relatively cheap and portable and is currently one of the most widely used non-invasive brain imaging tools in neuroscience and hospitals. In research, EEG time series are used in a wide range of fields, including medicine (e.g. diagnosis of epilepsy or the early detection of dementia), computer science (e.g. brain computer interfacing (BCI) and human activity recognition) and psychology (e.g. the study of cognitive development).
Our aim is to make sktime-neuro a collaborative mechanism and community hub for the scientific exploration of EEG/MEG. At the heart of many EEG related research questions is the problem of building a predictive regression or classification problem. This can be diagnostic (does the EEG record-ing of a patient indicate they have dementia?), descriptive (can we tell from the EEG recording whether an individual is moving their left or right arm?), or cognitive (is the subject looking at a picture of a face or random noise?). Each field has a range of related tasks and experimental structures, and each has a different default methodology.
Tony Bagnall (@TonyBagnall)
James Large (@James-Large)
Svea Myer (@SveaMeyer13)