[HTML][HTML] Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation

T Merk, V Peterson, R Köhler, S Haufe… - Experimental …, 2022 - Elsevier
Sensing enabled implantable devices and next-generation neurotechnology allow real-time
adjustments of invasive neuromodulation. The identification of symptom and disease …

Decoding movement from electrocorticographic activity: a review

K Volkova, MA Lebedev, A Kaplan… - Frontiers in …, 2019 - frontiersin.org
Electrocorticography (ECoG) holds promise to provide efficient neuroprosthetic solutions for
people suffering from neurological disabilities. This recording technique combines adequate …

[HTML][HTML] The impact of epilepsy surgery on the structural connectome and its relation to outcome

PN Taylor, N Sinha, Y Wang, SB Vos, J de Tisi… - NeuroImage: Clinical, 2018 - Elsevier
Background Temporal lobe surgical resection brings seizure remission in up to 80% of
patients, with long-term complete seizure freedom in 41%. However, it is unclear how …

Polymer integration for packaging of implantable sensors

Y Qin, MMR Howlader, MJ Deen, YM Haddara… - Sensors and Actuators B …, 2014 - Elsevier
Abstract Inexpensive, easy-to-process, light-weight polymer-based materials that are
biocompatible, mechanically flexible, and optically transparent have emerged as …

Spatio-temporal progression of cortical activity related to continuous overt and covert speech production in a reading task

JS Brumberg, DJ Krusienski, S Chakrabarti, A Gunduz… - PloS one, 2016 - journals.plos.org
How the human brain plans, executes, and monitors continuous and fluent speech has
remained largely elusive. For example, previous research has defined the cortical locations …

Human motor decoding from neural signals: a review

W Tam, T Wu, Q Zhao, E Keefer, Z Yang - BMC Biomedical Engineering, 2019 - Springer
Many people suffer from movement disability due to amputation or neurological diseases.
Fortunately, with modern neurotechnology now it is possible to intercept motor control …

Information systems opportunities in brain–machine interface decoders

JC Kao, SD Stavisky, D Sussillo… - Proceedings of the …, 2014 - ieeexplore.ieee.org
Brain-machine interface (BMI) systems convert neural signals from motor regions of the
brain into control signals to guide prosthetic devices. The ultimate goal of BMIs is to improve …

Rapid decoding of hand gestures in electrocorticography using recurrent neural networks

G Pan, JJ Li, Y Qi, H Yu, JM Zhu, XX Zheng… - Frontiers in …, 2018 - frontiersin.org
Brain-computer interface (BCI) is a direct communication pathway between brain and
external devices, and BCI-based prosthetic devices are promising to provide new …

The effects of spatial filtering and artifacts on electrocorticographic signals

Y Liu, WG Coon, A De Pesters… - Journal of neural …, 2015 - iopscience.iop.org
Objective. Electrocorticographic (ECoG) signals contain noise that is common to all
channels and noise that is specific to individual channels. Most published ECoG studies use …

Recording and decoding for neural prostheses

DJ Warren, S Kellis, JG Nieveen… - Proceedings of the …, 2016 - ieeexplore.ieee.org
This paper reviews technologies and signal processing algorithms for decoding peripheral
nerve and electrocorticogram signals to interpret human intent and control prosthetic arms …