Modeling, Architectures and Signal Processing for Brain Computer Interfaces
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1 Modeling, Architectures and Signal Processing for Brain Computer Interfaces Jose C. Principe, Ph.D. Distinguished Professor of ECE/BME University of Florida US versus Europe The vision and framework to effectively utilize BCIs is more advanced in Europe. BCIs are just one piece of more advanced hybrid bionic systems already under design. There are new avenues being pursued in BCIs that transcend the control communication US paradigm. There is very solid and systematic analysis and design to improve performance of BCIs.
2 Modeling - Aalborg sensogait Thomas Sinkjaer, Aalborg STIMULATOR Cuff electrodes Brain Spinal Cord Cortical Prosthesis Spinal and Peripheral Nerve Prosthesis Myo-Junction Prosthesis Modeling Hybrid Bionic Systems Scuola Superiore Sant Anna
3 Modeling Paolo Dario Modeling Paolo Dario Integrating the natural and the artificial: Hybrid Bionic Systems
4 Modeling - Cyberhand Paolo Dario Modeling - Cyberhand Tf LIFE electrodes Paolo Dario Proprioception Touch/ pain Results from a rabbit with 4 different Stimuli.
5 Modeling Paolo Dario Modeling Paolo Dario Jointly designed Hybrid Bionic Systems
6 Modeling Animat Lab (CEA) College de France Cognitive architectures (cortico-basal ganglia thalamus) Reinforcement learning (Actor critic and TD) Evolutionary neuro controllers Psikharpax Robur Jean Meyer Kodamat Note: CEA has a state of the art robotics program with tele-operation, Micro manipulation, movement perception, and autonomous robots. For the sake of time and space we will not review them. Modeling The Artificial Rat Psikharpax Sensory Equipment Navigation Visual compass Motor Equipment Visual Auditive Haptic (whiskers) Vestibular Odometry Energy Action Selection Integration Navigation/ Selection Simulation and robotic Implementation Rearing Prehension Eye rotation Head rotation Wheels (legs) Reinforcement learning Jean Meyer
7 BCI Modeling Modeling - IDIAP BCI Asynchronous Architecture: distributed intelligence Jose Millan
8 Modeling - IDIAP Error Recognition by P300. Jose Millan Fast recognition of errors, and reliable interaction (70% higher bit xfer) Modeling - IDIAP Improving spatial resolution Jose Millan
9 Modeling - Sant Anna Augmenting BCIs - commercial applications. BCIs are slow and should minimize the cognitive load (be non exclusive). To be useful they have to augment human s output pathways Modeling - Sant Anna
10 Modeling Sant Anna Natural Interfaces Eye-Head Coordination ~1 sec anticipation Modeling Sant Anna
11 Modeling Sant Anna Digital Signal Processing (DSP) for BCIs
12 Signal Processing- Aalborg BCI Features and adaptive kernels for single trial movement related potentials (MRP) Singlechannel EEG signal Optimization of feature extraction DWT Coefficient Transformation Optimization of classifier SVM Pe (training set) θ=[α1, α2,, αl] Kernel Parameters of the kernel Dario Farina Aalborg Signal Processing Aalborg Optimal wavelet vs Daubechies wavelet (same filter length, C4) Classification error (db wavelet) 50 Average misclassification 45 error on the test set (6 40 subjects) Classification error (optimal wavelet) 21.6 Optimized wavelet 26.9 Daubechies wavelet Dario Farina, Aalborg
13 Signal Processing - EPFL P300 BCI to learn environment control Adaptive Spatial filters (virtually created from the channels) Maximize distance of class means (GED) T. Ebrahimi Bayesian Linear Discriminant Analysis for a BCI Learns in one shot Signal Processing- Berlin Systematic analysis of variability of single event responses for BCIs: intra subject, inter subject and operand condition (open-close loop). Klaus Muller, Fraunhofer
14 Signal Processing - Berlin What features? Klaus Muller Faunhofer Signal Processing - Berlin What classifiers? Klaus Muller Faunhofer
15 Signal Processing - Berlin Results Klaus Muller Faunhofer Signal Processing - Freiburg Inference of movement direction from MUA and LFPs. Ad Aertsen, Freiburg
16 Signal Processing - Freiburg Performance of SUA and LFPs (8 channels) Single-trial decoding of movement direction Separate sets of training and test trials Quantification of decoding accuracy by percentage of correctly classified trials. Ad Aertsen, Freiburg Signal Processing - Freiburg Different classifiers From left to right: Penalized linear discriminant analysis, Support Vector Machine (SVM) with radial basis function kernel, SVM with linear kernel, Multivariate Gaussian Model, Population Vector. Ad Aertsen, Freiburg
17 Signal Processing - Freiburg Frequency bands of tuning Relative amplitudes (to baseline before cue) Relative ampitudes (mean across each band) Ad Aertsen, Freiburg Signal Processing - Freiburg How spatial information helps LFPs Ad Aertsen, Freiburg
18 Signal Processing Freiburg Cosine tuning prominent in all the three frequency bands! t = 50 ms before movement onset Hz Hz X 6 13 Hz 4 Hz Ad Aertsen, Freiburg Signal Processing - Freiburg Human experiments with ECoG grids (5 epileptic patients) Central sulcus Border between M1 and PM Ad Aertsen, Freiburg T=125 ms after movement onset
19 Signal Processing - Freiburg Movement related potentials are tuned Amplitude (µv) Direction (deg.) t (s) r 2 =0.84 (µv) t (s) r 2 =0.97 (µv) Direction (deg.) Direction (deg.) Ad Aertsen, Freiburg Signal Processing - Freiburg Movement direction can be decoded from MRPs Perimovement Activity Premovement Activity MC M1 PM PF adecoding power (%) c cdecoding power (%) targets 8 targets MC M1 PM PF Ad Aertsen, Freiburg chance level Single-trial decoding of movement direction Separate sets of training and test trials Quantification of decoding accuracy by probability of correct classification
20 Signal Processing - Freiburg Movement direction in ECoG and LFPs 200 Hz Directional information Intracortical (LFP) 150 Hz 100 Hz 60 Hz 30 Hz Epicortical (EFP) Ad Aertsen, Freiburg Frequency (Hz) a M t (s) Signal Processing Sta Lucia Improve spatial resolution of EEG thru functional neuroimaging Understand interplay between brain areas (function connectivity) by Granger causality
21 Steps to improve the spatial details of recorded EEG Data Realistic Insert the Model geometry of of The skullhead and Volume dura mater Conductor in inverse calculation From scalp to cortical EEG in RoIs Scalp EEG Linear inverse estimates within a RoI are collapsed (mean) M1 Hand area RoI Virtual electrode
22 Cortical estimation Conclusions I hope to have provided evidence for my assessment of the US Europe state of BCI research. I would like to thank the groups that hosted us in Europe for their hospitality, literature and frank discussions. I hope to have interpreted correctly the materials that were provided to us.
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