Motor-Errors Related Potentials for Autonomous Brain-Computer

Subject: Motor-Errors Related Potentials for Autonomous Brain-Computer
Faculties: Faculty of Education in Technology and Science
  Faculty of Mechanical Engineering
  Faculty of Medicine
Researchers: Associate Professor Miriam Reiner, Associate Professor Miriam Zacksenhouse and Professor Hillel Pratt


One of the promising applications of autonomous systems is in human rehabilitation and assistance, especially for severely disabled patients. Such systems require a reliable yet intuitive human interface that does not require neuromuscular control.

Brain Computer Interfaces (BCIs) have been extensively developed in recent years as a potential interface for a range of applications for human rehabilitation and assistance. Non-invasive BCIs captures brain signals through an electroencephalogram (EEG) recording, with an array of electrodes mounted in an EEG-cap . While initial applications of BCI focused mainly on spelling applications more recent applications extend to the control of semi-autonomous rehabilitation robots, the control of neuroprosthetic devices, and navigation of autonomous wheelchairs, and for gaming entertainment. BCIs are especially promising for severely handicapped patients since relevant EEG responses can be elicited even by motor imagery. This is the basis of the Berlin Brain-Computer Interface (BBCI).

The success of BCIs in interpreting human intention depends on proper preprocessing and machine learning techniques for both feature detection and classification. Typical success rates are around 70%. Remaining errors are detected and corrected by the user, thereby hampering the efficiency and transparency of the interface. To facilitate automatic error correction by the BCI itself it needs to detect the EEG activity associated with error detection by the user, and augment the control accordingly. While the importance and potential benefits of error detection for improving BCI systems has been noted, investigations focused mainly on detecting cognitive errors. Here we propose to investigate the existence and detection of movement related errors, which are expected to arise from estimation errors. The proposal is motivated by our recent results from invasive BCIs (known as Brain-Machine Interfaces, BMIs) which indicate that the modulations in the neural activity increases when Monkeys first started to use the interface, and especially after they stopped moving the hand . It is hypothesized that this enhancement is due to increasing variance of estimation errors and control signals. Most importantly, we hypothesize that this enhanced activity would result in detectable changes in EEG activity. Our proposal is targeted at detecting and characterizing EEG potentials related to motor-errors. Once detected these potentials can be used for on-line autonomous motor correction and enhance the application of BCIs handicapped patients.