Sridevi Sarma’s research focuses on a system with three components: electrodes implanted in the brain, which are connected by wires to a neurostimulator or battery pack, and a sensing device, also located in the brain implant, which detects when a seizure is starting and activates the current to stop it. (Credit: Illustration by Greg Stanley/JHU)
Epilepsy affects 50 million people worldwide, but in a third of these cases, medication cannot keep seizures from occurring. One solution is to shoot a short pulse of electricity to the brain to stamp out the seizure just as it begins to erupt. But brain implants designed to do this have run into a stubborn problem: too many false alarms, triggering unneeded treatment. To solve this, Johns Hopkins biomedical engineers have devised new seizure detection software that, in early testing, significantly cuts the number of unneeded pulses of current that an epilepsy patient would receive.
Epilepsy is a common neurological condition in which the normal electrochemical activity of the brain is disrupted resulting in seizures. The disease affects 1-2% of the worldwide population. According to Epilepsy Australia, it is estimated that over 180,000 Australians are living with epilepsy, approximately 2% of Australians will experience the condition at some point in their lives and up to 5% may experience a one-off epileptic seizure. Epilepsy is controlled, but not cured, by medication, and around 30% of sufferers do not respond well to medication.
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About the OpenEEG project
Many people are interested in what is called neurofeedback or EEG biofeedback training, a generic mental training method which makes the trainee consciously aware of the general activity in the brain. This method shows great potential for improving many mental capabilities and exploring consciousness. Other people want to do experiments with brain-computer interfaces or just want to have a look at their brain at work.
Unfortunately, commercial EEG devices are generally too expensive to become a hobbyist tool or toy.
Electroencephalography (EEG) records the electrical signals produced by the brain using an array of electrodes placed on the scalp. Computers use an algorithm called common spatial pattern (CSP) to translate these signals into commands for the control of various devices.
Haiping Lu at the A*A*STAR Institute for Infocomm Research and co-workers[1] have now developed an improved version of CSP for classifying EEG signals. The new algorithm will facilitate the development of advanced brain–computer interfaces that may one day enable paralyzed patients to control devices such as computers and robotic arms.
