Journal: Experimental neurology

Article Title: Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

doi: 10.1016/j.expneurol.2022.113993

Figure Lengend Snippet: Schematic representation of a conventional adaptive deep brain stimulation algorithm compared to a machine learning-based adaptive deep brain stimulation system. (A) Input signals are cortical ECoG and subcortical LFP brain signal recordings. New incoming data packets are preprocessed (e.g. normalization, rereferencing, artifact detection and subsequent rejection applied) and features are extracted (e.g. Fourier transformation, band power averaging and smoothing). The control algorithm is a simple threshold detection of a predefined feature: the brain state (e.g. pathological or non-pathological state) is predicted, and translated into a control command, such that the DBS stimulation parameters are adapted. (B) Machine learning-based adaptive deep brain stimulation can use multimodal features to decode a variety of brain states (e.g. classification for decoding of tremor or regression for indication of severity of bradykinesia in PD). In addition to brain signals, the decoding model can also be re-adjusted based on the information delivered by the stimulation signal. Moreover, information from previous patients could be used to feed the decoding algorithms and potentially avoid time-consuming individual training sessions.

Article Snippet: Real-time decoding implies additional machine learning model constraints for adaptive neuromodulation.

Techniques: Transformation Assay, Control

Journal: Experimental neurology

Article Title: Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

doi: 10.1016/j.expneurol.2022.113993

Figure Lengend Snippet: Architecture of a representative machine-learning pipeline. During model training, features are extracted from training data. The most relevant features can then be selected. The prediction model, either for classification or regression, is based on optimized parameters that transform input features into predicted model output. During training, parameters are optimized, until the performance saturates, and no further improvement is gained. Once the model yields satisfactory performance metrics on training data, the learned parameters can be directly applied to new input features for test set model predictions. A good decoding model is a model in which training and testing performance remain similar. Deep learning architectures enable feature construction and selection within the model training step.

Article Snippet: Real-time decoding implies additional machine learning model constraints for adaptive neuromodulation.

Techniques: Selection

Journal: Experimental neurology

Article Title: Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

doi: 10.1016/j.expneurol.2022.113993

Figure Lengend Snippet: Overview of common Machine learning model architectures. The most commonly used machine learning models for invasive neural decoding are shown, ranging from simple linear methods to more complex models. Each method is built under different model assumptions and comes with specific advantages and disadvantages. Interpretability of the solution plays a key role in invasive neuromodulation.

Article Snippet: Real-time decoding implies additional machine learning model constraints for adaptive neuromodulation.

Techniques:

Journal: Experimental neurology

Article Title: Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

doi: 10.1016/j.expneurol.2022.113993

Figure Lengend Snippet: Machine learning studies for decoding of physiological states from intracranial recordings.

Article Snippet: Real-time decoding implies additional machine learning model constraints for adaptive neuromodulation.

Techniques: Extraction, Injection