This research was completed by researchers at the Swiss Federal Institue of Technology Lausanne (EPFL).\u00a0Professor Aude Billard<\/a>, the head of EPFL\u2019s Learning Algorithms and Systems Laboratory and Jos\u00e9 del R. Mill\u00e1n<\/a>, previously the head of EPFL\u2019s Brain-Machine interface Laboratory, worked together to create a computer program that can control a robot using electrical signals from a patient\u2019s brain.<\/p>\n The patient\u2019s brain activity was monitored by an EEG cap \u2013 which effectively scans the electrical activity inside your head. These brain waves would then be sent through a computer to be interpreted by the machine-learning algorithm. The algorithm translates the brain signals when the patient notices an error, inferring automatically when the brain doesn\u2019t like a certain action.<\/p>\n In the team\u2019s research, they used the robot arm with a glass. The arm would move towards the glass and the patient\u2019s brain would decide if they felt it was too close or too far away. The process is repeated until the robot understands the\u00a0 optimal route for the indiviudal\u2019s preference \u2013 not too close to be a risk but not so far away to waste movement.<\/p>\n \u201cThe brain signals that we are recording will be never be the same. We have a variability over time and this is natural. Why? Because if I move my hand, the brain is not only focused on that, the brain is processing many other things,\u201d said Mill\u00e1n. \u201cSo the fact that there is this variability means that our decoder will never be 100 per cent accurate.\u201c<\/i><\/p>\n However, through the machine-learning algorithm used in this research, the robot can gain a better understanding of variability to predict brain signals in certain situations. For example, the distance preference when moving past a glass or, in a practical circumstance, how close a tetraplegic patient in a wheelchair is willing to get to other people in the street.<\/p>\n Implementing the algorithm into a wheelchair is an example of where the technology could go in the future. This would allow people in wheelchairs to have greater control over their movements, speeds and general safety. The algorithm could interpret brain signals to understand a user\u2019s speed preference, the distance they are happy to be from obstacles and people and even the level of risk they are willing to take in certain circumstances, for example if they\u2019re late or somewhere busy.<\/p>\n \u201c<\/i>It\u2019s interesting to use this algorithm over using speech, for instance, because there are things that you cannot necessarily easily articulate,\u201d said Billard. \u201cA layperson may not be able to articulate that they don\u2019t like the acceleration of a wheelchair for example. What is it that you don\u2019t like exactly? How does that translate into a control parameter afterwards?\u201d<\/p>\n This is where the technology stands out from other disability aids available. By allowing the algorithm to understand the signals from your brain, it can interpret exact feelings that an individual couldn\u2019t explain themselves. However, this does require consistency over time from the algorithm and for the detection to have proved statistically significant.<\/p>\n Without that consistency, the algorithm could be thrown off in real life situations. If, for example, someone was driving a wheelchair in a crowd and passed people in an argument, the person could generate an error which has nothing to do with the driving experience.<\/p>\n![\""\u00a9\"](\""https:\/\/images.immediate.co.uk\/production\/volatile\/sites\/4\/2022\/01\/robot-photos-d2e909d.jpeg?quality=90&resize=620%2C413"\" "\\"\"robot\\"")
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