Have you ever imagined that a person who lost the ability to speak and cannot pronounce words clearly would be able to communicate what they want to say?
This has come true with the incredible advances in artificial intelligence and machine learning. Various new researches are being carried out and many algorithms are being instituted to meet the different needs of humanity. Researchers at Stanford University have developed one such interface. This brain-computer interface can help a person with a condition such as paralysis to transmit their thoughts and communicate at 62 words per minute.
Brain-Computer Interface (BCI) is simply defined as a device that allows users to interact with computers solely through brain activity. It is a direct pathway with the help of which the electrical activity of the brain tries to communicate with a foreign device. This external device is mainly a computer or a robotic limb. In Artificial Intelligence, BCI measures the activity of the central nervous system (CNS) and converts it into an artificial output. This output replaces and enhances the natural output of the central nervous system, modifying the interactions between the CNS and the external environment.
The Stanford researchers used the Recurrent Neural Network (RNN) to process the Brain-Computer Interface, making it capable of synthesizing speech from signals found and captured in a patient’s brain. Compared to previously existing BCI approaches that allow for speech decoding, this latest method allows a person to communicate at 62 words per minute, which is 3.4 times faster than previous ones. With artificial intelligence revolutionizing and entering all fields like healthcare and medicine, this new speech-to-text interface can help people who are unable to produce clear speech to communicate effectively.
The researchers have shared that the system has been demonstrated in a person suffering from loss of speech ability due to amyotrophic lateral sclerosis (ALS). The system has been processed by the formation of RNN, specifically a Closed Recurrent Unit (GRU) model. The team tried to capture the words spoken by the patient as she tried to speak by using intracortical microelectrode arrays implanted in the patient’s brain. These microelectrode arrays record signals with a resolution of a single neuron. These signals were then transferred to the GRU model to decode speech.
The researchers mentioned that when the RNN model was trained on a limited vocabulary of 50 words, the BCI system showed an error rate of 9.1 percent. After increasing the vocabulary to 125k words, the error rate changed to 23.8%. The error rate improved to 17.4% by adding a language model to the decoder. The total data the team collected for training purposes was 10,850 sentences which were performed by displaying a few hundred sentences every day for the patient to say. The microelectrodes captured the neural signals as soon as the patient uttered the sentences.
This system is definitely a breakthrough in BCI work, as a lot of research is being done to decipher brain activity. This development can greatly help patients with paralysis, stroke, etc. With 3.4 times better performance than currently existing approaches, this system can work wonders.
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Tanya Malhotra is a final year student at the University of Petroleum and Power Studies, Dehradun, studying BTech in Computer Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.
