Decoding EEG and LFP signals using deep learning: heading TrueNorth

Deep learning technology is uniquely suited to analyse neurophysiological signals such as the electroencephalogram (EEG) and local field potentials (LFP) and promises to outperform traditional machine-learning based classification and feature extraction algorithms. Furthermore, novel cognitive computing platforms such as IBM's recently introduced neuromorphic TrueNorth chip allow for deploying deep learning techniques in an ultra-low power environment with a minimum device footprint. Merging deep learning and TrueNorth technologies for real-time analysis of brain-activity data at the point of sensing will create the next generation of wearables at the intersection of neurobionics and artificial intelligence.

[1]  Dharmendra S. Modha,et al.  Backpropagation for Energy-Efficient Neuromorphic Computing , 2015, NIPS.

[2]  Greg Worrell,et al.  Long-Term Measurement of Impedance in Chronically Implanted Depth and Subdural Electrodes During Responsive Neurostimulation in Humans , 2013, Brain Stimulation.

[3]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[4]  J. Wolpaw,et al.  Brain-Computer Interfaces: Principles and Practice , 2012 .

[5]  Andrew S. Cassidy,et al.  Cognitive computing programming paradigm: A Corelet Language for composing networks of neurosynaptic cores , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[6]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  David M. Himes,et al.  Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study , 2013, The Lancet Neurology.

[8]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Robin C. Ashmore,et al.  An Electrocorticographic Brain Interface in an Individual with Tetraplegia , 2013, PloS one.

[10]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[11]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[12]  Gerald Penn,et al.  Applying Convolutional Neural Networks concepts to hybrid NN-HMM model for speech recognition , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[14]  Qiang Ji,et al.  Deep Feature Learning Using Target Priors with Applications in ECoG Signal Decoding for BCI , 2013, IJCAI.

[15]  Philippa J. Karoly,et al.  Seizure Prediction: Science Fiction or Soon to Become Reality? , 2015, Current Neurology and Neuroscience Reports.

[16]  John Pavlus.  THE SEARCH FOR A NEW MACHINE. , 2015, Scientific American.

[17]  Jonathan R. Wolpaw,et al.  Brain–Computer InterfacesPrinciples and Practice , 2012 .

[18]  David B. Grayden,et al.  A Generalizable Brain-Computer Interface (BCI) Using Machine Learning for Feature Discovery , 2015, PloS one.

[19]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[20]  A. P. Georgopoulos,et al.  Neuronal population coding of movement direction. , 1986, Science.

[21]  Nicolas Y. Masse,et al.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.

[22]  Justin A. Blanco,et al.  Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement , 2011, Journal of neural engineering.

[23]  Nima Mesgarani,et al.  Speech reconstruction from human auditory cortex with deep neural networks , 2015, INTERSPEECH.

[24]  Kapil D. Katyal,et al.  Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject , 2016, Journal of neural engineering.

[25]  Andrew S. Whitford,et al.  Cortical control of a prosthetic arm for self-feeding , 2008, Nature.

[26]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Michael Breakspear,et al.  Dynamics of a neural system with a multiscale architecture , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[28]  Jack Cowan,et al.  Neural Control Engineering: The Emerging Intersection Between Control Theory and Neuroscience , 2012 .

[29]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[30]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

[31]  D. Chialvo.  Emergent complex neural dynamics , 2010, 1010.2530.

[32]  Benjamin Schrauwen,et al.  Audio-based Music Classification with a Pretrained Convolutional Network , 2011, ISMIR.

[33]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .