Technology to move objects with the mind

EEG-powered brain computer interfaces have the potential to allow severely disabled people to operate home automation systems, wheelchairs, and other technologies. Yet, anyone who has tried playing games controlled through EEG knows that it’s not easy to get one’s mind in a specific state every time you want to. So it can be a challenge that frustration can only exacerbate, resulting in mental fatigue and poor performance of the system. Peñaloza Sanchez, a PhD student at University of Osaka in Japan, has been working on overcoming this problem by building a learning mechanism into an EEG brain-computer interface.

The system attempts to recognize user patterns and eventually takes short cuts when it notices the person trying to achieve a task done previously. The EEG system works in conjunction with environmental sensors placed around the room the user would be in, feeding info like room temperature, whether doors are open or closed, and if appliances are left on. The system tries to predict what the user wants to do based on earlier recordings, and activates a series of actions such as to navigate a wheelchair to a certain spot or to turn off the lights. In order to prevent frequent mistakes by the over eager system, there are algorithms built in that can identify so-called “error-related negativity,” or the brain’s reaction when it notices a mistake being made. When such a signal is detected during the system’s automatic activity, it’s a sign that it’s making a mistake and the system cancels its current action to try again.

Reduce Mental Fatigue

Systems able to process thoughts and translate them into a command to move objects are very useful for people who cannot speak or move, but have the disadvantage of causing mental fatigue. However, a Mexican researcher designed an intelligent interface that is capable of learning up to 90 percent of the user's instructions thus operate autonomously and reduce fatigue.

This project, called "Automating a brain-machine interface system", is in charge of Christian Isaac Peñaloza Sanchez, a PhD candidate for Cognitive Neuroscience Applied to Robotics at the University of Osaka, Japan.

He explains that the system consists of electrodes placed on the scalp of the person, which measure brain activity in form of EEG signals. These are used to detect patterns generated by various thoughts and the mental state of the user (awaken, drowsy or asleep, etc.) and level of concentration.

It also includes a graphical interface that displays the available devices or objects, which interprets EEG signals to assign user commands and control devices.

"We've had pretty good results in various experiments with multiple people who have participated as volunteers in our in vivo trials. We found that user mental fatigue decreases significantly and the level of learning by the system increases substantially," the researcher says.