Addressing the common disadvantage of today’s brain-computer interfaces, namely the increased mental fatigue caused to the user, Japanese scientists and a Mexican researcher have developed the first BCI device capable of learning up to 90% of the user’s instructions, in order to operate autonomously and reduce mental fatigue.The system, designed to help people with severe motion or speaking disabilities, is the first of its kind addressing the excessive mental load that existing BCIs place on a user. Every time the user wants to perform an action, he or she has to focus their mental energy to deliver the message, which could be very tiring.
“We give learning capabilities to the system by implementing intelligent algorithms, which gradually learn user preferences,” said Christian Isaac Peñaloza Sanchez, a PhD candidate at the University of Osaka, Japan.
“At one point it can take control of the devices without the person having to concentrate much to achieve this goal,” he said.
The new learning BCI can ‘understand’ relatively simple commands, such as turning on a TV or making a wheelchair move, and learn the user’s mental commands. The system learns when an action is not as intended – such as turning on the radio instead of the TV – and can judge using EEG meters, that the user is reacting negatively to the action and responds accordingly.
After the system learns the command from the user, the action could be triggered either by pressing a button or by a quick thought. While performing the automated action, the system looks for the so-called error-related negativity signal – a reaction in a human brain when an incorrect response is initiated – for example if the system opens a window instead of turning on the TV.
The system could be used to control a whole range of devices – robot prosthetics, computer pointers or house appliances. It also includes a graphical interface displaying the available devices or objects, which interprets EEG signals to assign user commands and control devices.