Clinical Applications

Students use EEG data to develop algorithms used to detect states of alertness using their own real-time EEG signals. States such as eyes open or closed or relaxed or active mental state are evaluated and advantages and disadvantages of this method are evaluated.

Students use their accumulated knowledge to contemplate biofeedback applications. Biofeedback experiments allow for the exploration of signal processing methods and students learn how to combine information form different biopotentials to create a feedback signal.

The linear relationship between the DC component of the EOG signal and the position of the eye is learned. The EOG signal is calibrated and used to control the position of a dot on the screen. This application correlates to computer cursor control for an individual with a high level spinal chord injury.

EMG data is collected from the legs while walking and the processed signals are used to calculate stride time. Features from the data are compared with abnormal gait data from patients with Parkinson’s disease, Anterior Lateral Sclerosis and Huntington’s disease.

Using a three-lead ECG recording, students design a simple real-time heart rate detector using a threshold detection value of the QRS complex. The advantages and disadvantages are discussed and the development of more robust algorithms is explored.

EMG from the biceps and wrist extensor muscles is measured and used to control a virtual robot arm on the screen. Processing methods are applied, such as low and high pass digital filtering, and the affect on the control of the robot arm is examined. This is related to algorithms used to control myoelectric prosthetics after amputations.

Students used all acquired skills to design their own laboratory. The use of LabVIEW and MATLAB drivers is encouraged, allowing students to create their own real-time software application using the BioRadio.