Dissertation > Excellent graduate degree dissertation topics show

A Study on Multi-modal and Multiple Degree-of-Freedom Brain-computer Interfaces

Author: YuTianYou
Tutor: LiYuanQing
School: South China University of Technology
Course: Pattern Recognition and Intelligent Systems
Keywords: Brain-computer interface EEG Hybrid BCI P300 Motor imagery Adap?tive P300speller BCI browser BCI mail client
CLC: TN911.7
Type: PhD thesis
Year: 2013
Downloads: 85
Quote: 0
Read: Download Dissertation

Abstract


Brain–computer interfaces provide non-muscular communication and control bydirectly translating brain activities recorded from the scalp into computer control sig-nals and thus enable users with motor disabilities to convey their intent to the externalworld. An important issue in BCI research is multidimensional control, which enablesconsiderably enhanced interfacing between the user and the machine. Potential applica-tions include BCI controlled computer mouse, wheelchair, neuroprothesis, etc. To date,most of the BCIs for multidimensional control have been invasive or with expensive neu-roimaging devices like magnetoencephalography. On the other hand, the developmentof noninvasive EEG-based multidimensional control BCI is impeded by the difficulty inobtaining multiple independent control signals from the noisy EEG data of poor spatialspecificity.Several studies have shown that hybrid BCIs may yield a better performance thanBCIs that use only one type of brain signal. The main goal of this study is to developmulti-modal (hybrid) BCIs and to provide multiple degree-of-freedom (DoF) output, thusprovide multidimensional control, by them. Emulating a computer mouse with BCI is atypical example. Here, the first task is to move a mouse from an arbitrary initial positionto an arbitrary target position based on two independent signals from EEG, which arefor the vertical and horizontal movement of the cursor respectively. The second task isto select the target-of-interest and reject the target-of-no-interest, as fast and accurate aspossible.By combining P300and mu rhythm, a hybrid BCI system for2-D cursor movementcontrol is first proposed. The BCI system simultaneously performs detection of P300andmotor imagery, and uses the detection outputs to control the cursor’s movement in a2-Dworkspace. The users move the mouse up/down by attending to one of the three flashing"UP"/"DOWN" buttons in a specially designed GUI. Meanwhile, they move the mousetoward right by imagining his right hand and vice versa. Once the cursor hits a target,the system makes a decision of selection/rejection of the target. The target selectionis implemented by combining motor imagery and P300into a hybrid feature, which isfurther classified to make the decision. If the target is of interest, the user can selectit by focusing on the flashing "STOP" button on the left side without motor imagery. Otherwise, he can reject it by motor imagery without focusing on any flashing buttons.In this way, a BCI mouse was implemented.Based on the BCI mouse, we developed a BCI browser, by which the user can selectan intended target in a multi-target web page. A target iflter based on the adaptiveP300speller (for keywords input) was introduced to exclude most of the targets-of-no-interest. A series of mouse movements and selections/rejections were performed to selectthe intended target. Seven healthy subjects attended the online experiment and all ofthe subjects can open a web page and select items-of-interest in it using our system.We also proposed a BCI mail client for electronic mail communication based on thehybrid BCI mouse. The adaptive P300speller was embedded into this system for effectivetext input. Six subjects attended the online experiment and successfully received，read,wrote emails and attached ifles with this system.An adaptive P300speller is then proposed. The P300speller adaptively selects thenumber of epochs to average, according to the subject’s current performance. With thisP300speller, we won the championship of the Character Input event in the1st ChinaBCI Competition (2010). Our subject entered72characters in6minutes with1error.

Related Dissertations

  1. A Study on the Coma Scales and Electroencephalogram for the Prediction of the Comatose Patient,R741.044
  2. Evaluation of Cognitive with Dementia Assessment Scale and the Related Potential P300in Mild Cognitive Dysfunction Patients,R749.1
  3. Characteristics of sensory stimulation evoked,R318.0
  4. A Study of Neuroelectrophysiology in Patients with Depression Disorder and Patients with Generalized Anxiety Disorder,R749.4
  5. The Change of Hearing P300 and the Mismatch Negativity in the Newly Parkinson’s Disease Patient Around Treatment and the Correlatead Research,R742.5
  6. Activation of RASSF2 by p300 Induces S/G2 Cell Cycle Arrest in Gastric Cancer Cell HGC-27,R735.2
  7. Paradigm Design and Algorithm Research for P300-based BCI,TP334.7
  8. Preliminary Study of MicroRNA Expression in Hippocampus of Rats with Cognitive Disorders After Traumatic Brain Injury,R651.15
  9. The Effection Study on Visual Event-related Potentials P300 in Evaluating Clinical Character of Elderly Depression,R749.41
  10. Research of the Mental Fatigue Classification Based on HHT and Kernel Method,TN911.7
  11. Modeling and Controlling of Cortical Signals and Pneumatic Artificial Muscles Using the Echo State Networks,TN911.7
  12. Mapping of Image Emotional Feature Based on Eeg,TP391.41
  13. Study on Relationship Between Depression and Cognitive Functions in Parkinson’s Disease,R742.5
  14. Analysis of Sleep Stage Classification Based on Symbolic Transfer Entropy and Average Dissipated Energy,R318
  15. Digitally Trimmable Analog Front-End Chip Design for EEG Signal Acquisition,TN402
  16. The Fatigue Estimation and Real-Time Monitoring Based on Eeg,TN911.6
  17. Brain Computer Interface for Virtual Navigation Based on Motor Imagery and Alpha Waves,TP334.7
  18. EEG -based emotion recognition,TP391.4
  19. Neural electrophysiological dress Contact Comfort Research,Q42
  20. Machine learning method based automatic classification of EEG,TP181
  21. Research and design of the television remote control system based on brain-computer interface technology,TP872

CLC: > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Communication theory > Signal processing
© 2012 www.DissertationTopic.Net  Mobile