1 / 32

last lecture: Foundations of DSP Basic Operations: convolution Digital Filters: FIR and IIR Some Classification methods

last lecture: Foundations of DSP Basic Operations: convolution Digital Filters: FIR and IIR Some Classification methods Biosignal Libraries and Applications Practical Demonstrations: Matlab and FiView Review of Project exercises Firmware - programming. Today:

allie
Télécharger la présentation

last lecture: Foundations of DSP Basic Operations: convolution Digital Filters: FIR and IIR Some Classification methods

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. last lecture: • Foundations of DSP • Basic Operations: convolution • Digital Filters: FIR and IIR • Some Classification methods • Biosignal Libraries and Applications • Practical Demonstrations: Matlab and FiView • Review of Project exercises • Firmware - programming

  2. Today: Biofeedback: Principles and Applications Brain Computer Interfaces – Theory and Methods Sources and further reading: Winfried Rief, Niels Birbaumer: Biofeedbacktherapie - Grundlagen, Indikation und praktisches Vorgehen, Schattauer, 2000, ISBN 3-7945-1968-X Brendan Allison - Collected BCI material : http://www.cis.gsu.edu/brainlab/PapersOtherWritings.htm

  3. Biofeedback: ● Transformation of physiological processes into visual, acoustic and / or sensual information Biofeedbacktherapy: ●Concious regulation of vital processes by learning how to change the feedback values http://www.hawbs.org/gallery/images/oxqoe_biofeedback.jpg

  4. Potentials of Biofeedbacktherapy: ●Increased relaxation ● Increased self-awareness ● Increased expactation of self-efficiency ● Correction of malpositions ● Prevention of overreactions (physical as mental) ● Support for psycho- and/or physiotherapy ● Reduction of medication ● Reduction of side-effects ● Good (patients-) acceptability http://www. Biofeedbacktherapie.info

  5. Key-elements for a successful Biofeedback treatment: ●Expierienced therapist knowledge about psycho-physical processes and interrelations knowledge about correct measurement of vital parameters knowledge about problematic levels of vital parameters knowledge about problematic levels of vital parameters knowledge about desired levels of vital parameters ability to establish patient‘s trust and motivation ●Intregration into a holistic Therapy support of a conventional therapy ( medication / psychic treatment ) ●Intregration of achievements into everyday life

  6. Vital Parameters that can be modified by • Biofeedback-Training: • Muscle activity EMG • Heart rate ECG • Breathing patterns breathing belt • Blood pressure Plethysmography • Vessel constriction and dilation • Peripheral blood flow • Perspiratory gland activity (sweat) GSR / EDA • Skin- and body temperature Peripheral Body Temp. • Brainwave activity EEG

  7. Biofeedback for treatment of chronic back pain: Problems: ●very frequent pain illness ●common malpositions: shortening of tonic muscles, degradation of phaseal muscles ●spinal disc deformation ●vicious circle: pain increases muscle tension ●condition to neutral stimuli

  8. Biofeedback for treatment of chronic back pain: Methods: ●EMG-Feedback ●scanning of relevant muscle groups ●left/ right comparison ●find relations of (psychophysical) cause and tension ●force- and stress tests, stress profiles ●repeated EMG-sessions for distension / relaxation

  9. Biofeedback for tension headache / migraine Problems: ●muscle tension in the head / neck / chin areas ●instability of blood vessel regulation, high sensitivity to stress excessive vaso-constriction, followed by dilation ● inflammation and excited pain receptors

  10. Biofeedback for tension headache / migraine Methods: ●EMG, EDA and body temperature feedback: relaxation techniques ●Peripheral blood flow / blood volume pressure feedback: training of vessel dynamics, vasoconstriction-training ●60 % of the patients achieve a reduction of > 50% migraine attacks [BIR]

  11. Biofeedback for anxiety disorders or • psychosomatic syndromes • Problems: • ● Somatic pain without any physical or pathological reason • ● Hypochondric attention to normal changes or processes • ● Drug addiction / addictive behaviour • Methods: • ●Biofeedback to increase the self-efficiency hypothesis / awareness • ●change the organic explanatory model into a psychophysical one • ●accomplish stress (exposition-) tests with biofeedback monitoring • ●relaxation training and systematic de-sensitization • ●biofeedback as a support for psychic therapy

  12. Biofeedback for Tinnitus • Problems: • ● permanent handicap due to loud tones / noise • ● loss of hearing / deception • ● bruxism (tooth chrunching) • ● sleep disorders / depression • Methods: • ●EMG training of chin and neck muscles • ●EDA and peripheral body temperature training: relaxation

  13. Biofeedback for Problems of Incontinence • Problems: • ● dysfunctional pelvic floor • ● pelvic floor degeneration after birth / due to high age • ● problems with urine and stool retention • ● common handicapbut big social problem

  14. Biofeedback for Problems of Incontinence • Methods: • ● training of detrusor activity using a pressure sensor in the bladder • ● training ofpelvic floor / sphincter muscles • ( rectal/vaginal EMG – probes, ballon-catheter ) • ● training of coordination of the abdominal muscle • ( surface and rectal / vaginal EMG ) • -> increase of contraction strength, -duration and coordination

  15. Biofeedback for neural lesions • Problems: • ● motor-disabilities after stroke or ischemic insult • ● neural / muscle degeneration after accidents • ● loss of control / muscle spasms • Methods: • EMG Biofeedback to support rehailitation training : • ● bridge lost (physical) feedback mechanisms • ● regain muscle control due to improvement of intrinsic perception • and constant training (feedback gives motivation) • ● active support: EMG triggered functional electro stimulation (FES)

  16. Neurofeedback • ● re-establish physiological distribution • of brainwave frequencies in ADHD-patients • (Attention deficit hyperactivity disorder) : • - increased Theta, decreased Beta • - strong asymmetric distributions • - strong frontal alpha waves • ● treatment of sleep disorders, stroke, depression, tourette syndrome • ●training of positivation of slow cortical potentials can lower • epileptic seizures • ● relaxation, meditation and peak performance training • (Alpha / Theta – feedback )

  17. Brain Computer Interfaces (BCIs)

  18. Brain Computer Interfaces ● allow patients to control a computer by concious changes of brain activity ● provide a means of communication to completeley paralysed patients: amyotrophic lateral sclerosis (ALS), cerebral palsy, locked in syndrome ● can be used to control a cursor, select symbols, control external devices like orthesis / prothesis (depending on type of BCI) ● have a very low data rate, typical a few bit per second or less ● first results in the 1970ies (Vidal, visual evoked potentials, VEP-BCI)

  19. Brain Computer Interfaces - Brain / Cortex Topography: Sensomototic humunculus: (top) frontal lobe, gyrus precentralis http://www.neuroskills.com/brain.shtml

  20. Brain Computer Interfaces - Brain / Cortex Topography: Right and left brain map http://members.shaw.ca/hidden-talents/brain

  21. Brain Computer Interfaces Principles of operation:

  22. Brain Computer Interfaces – Major Types ● SCP Slow Cortical Potentials ● Mu Movement Imagination ● P300, SSVEP ERP-Analysis ● cortical neurons, direct brain interfaces The control information is extracted from the real time EEG-recording http://www.wired.com/news/images/full/thoughtlock1_f.jpg

  23. Brain Computer Interfaces – SSVEP ● Steady State Visual Evoked Potentials derived from the visual (occipital) cortex ● focussing attention to visual stimuli of different frequency shows up in the EEG freqeuncy bands ● relibable and high transfer rate, but some prerequisites (eyes) http://www.iua.upf.es/activitats/ semirec/semi-Reilly/

  24. Brain Computer Interfaces – SCP BCIs ● detection of slow cortical potentials (SCPs) ● needs DC EEG Amplifiers (no highpass filter) ● first successful device end 1990‘s: Niels Birbaumers Thought translation device intensive training with necessary to gain control over the SCP waves SCPs: DC-shifts, slow negativation of cortical areas Preparation of movement and cognitive tasks, Several hundert milliseconds before the task Patinet using TTD to write a letterhttp://www.heise.de/ct/06/18/088/bild1.jpg

  25. Brain Computer Interfaces - μ-rhythm BCIs ● μ–rhythm is the idle-rhythm of the motor cortex ● frequencies around 10 and 18 Hz, location : gyrus praecentralis individual differences -> multichannel EEG (QEEG) for offline analysis ● ERD / ERS – event related desynchronisation / synchronisation movements or imagination of movements inhibit the μ–rhythm Berlin-BCI, http://www.fraunhofer.de/ ERD/ERS at around 10, 22 Hz

  26. Brain Computer Interfaces - μ-rhythm BCIs ● two dimensional cursor control using different frequency bands for vertical horizontal movements (Wadsworth BCI) ● control of an orthesis, adaptive algorithm (Graz BCI) CSA of Mu-rhythms, http://www.robots.ox.ac.uk/ Wadsworth BCI, 2 dimensional control Graz BCI, orthesis

  27. Brain Computer Interfaces - P300 BCIs ● P300 wave – posivite component in the event related potential, 300ms after a stimulus ● natural response to events considered as important ● selection of a symbol: count the flashes, algorithm averages trails and finds a P300 P300 runtime user interface

  28. Brain Computer Interfaces - μ / P300 comparison μ - BCIs P300 BCIs Require training do not require training Work in realtime require averaging 2d-control possible 1D control only Continous control discrete control movement imagination concentration / decision affected by movement affected by distraction

  29. Brain Computer Interfaces - direct brain interfaces ● Electrocorticogram: implanted electrode array ● better signal quality , increased SNR radio transmission of signals ● problems: decreasing signal quality, risk of infection, invasive technique Components of a DBI system, P.R. Kennedy, R.A. Bakey, M.M. Moore, 1987, IEEE Trans Rehabil Eng. 8 (2):198-202 ECoG electrode grid photo by Gerwin Schalk (Wadsworth Center, Albany, USA), Kai Miller, Jeff Ojemann (University of Washington)

  30. Brain Computer Interfaces - μ / P300 comparison The first commercially available BCI system is actually an Austrian product: ● Mobile BCI using a pocket PC ● Matlab-based software for host-PC operation ●μ-BCI with training phase ●Supports digital I/O-lines ●Wireless transmission http://www.gtec.at/content.htm

  31. Brain Computer Interfaces - BCI2000 ● Research Platform for BCI Systems ●Written by Gerwin Schalk, Wadsworth Center, Albany (NY) ●Modular structure: Signal Aquisition, Signal Processing and User Application communicatie via TCP/IP ●Operator module used for configuration of the other modules ● various user tasks availbale: 1D/2D cursor, Speller, P300, SCP ● free for academic use ● driver for OpenEEG available http://www.bci2000.org/

  32. Brain Computer Interfaces - ten ways to improve BCIs: ● Better recording techniques ● Better understanding of EEG ● New brainwave parameters / hybrid BCI ● Customization of BCIs to each user ● Better pattern recognition ● Improved interfaces ● Better noise rejection ● Further testing with patients ● Studies on effects of training and long term use ● Improvements in computing and electronics „BCI development is an interdisciplinary problem, involving neurobiology, psychology, engineering, methematics, computer science and clinical rehabilitation“ Wolpaw et al, 2002

More Related