Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation
Autor(in); Institution
Merk, Timon; Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, GERMANY
Peterson, Victoria; Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, UNITED STATES
Köhler, Richard; Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, GERMANY
Haufe, Stefan; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin; Berlin Center for Advanced Neuroimaging (BCAN), Charité – Universitätsmedizin Berlin, Berlin, GERMANY
Richardson, R. Mark; Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, UNITED STATES
Neumann, Wolf-Julian; Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, GERMANY
Adaptive deep brain stimulation ; Brain-computer interface ; Closed-loop DBS ; Movement disorders ; Neural decoding ; Real-time classification
Zusammenfassung
Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief
introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available
datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.
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Open Access Hybrid
Rechteinformation
CC BY 4.0 ; Creative Commons Attribution 4.0 License
Themenbereich der Metrologie
Metrologie in der Medizin
Zitierung
Merk, T., Peterson, V., Köhler, R., Haufe, S., Richardson, R. M., & Neumann, W.-J. (2022). Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation. Experimental Neurology, 351, 1–17. https://doi.org/10.1016/j.expneurol.2022.113993
