AI in Neuroscience

date

Sep 11, 2023 04:17 PM

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AI-in-Neuroscience

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Public

AI in Neuroscience: Converting Brain Signals Into Text, Audio, and Images

Artificial Intelligence (AI) has revolutionized countless industries, and neuroscience is no exception. AI opens avenues for deep learning and advanced pattern recognition methods that are instrumental in making sense of complex brain signals. Diversified application of AI in neuroscience ranges from control of robotic arms by paralyzed individuals to brain signal translation into text, audio, and images. This report delves into how AI assists in these tasks, unraveling the intricate relationship between neuroscience and AI.

Converting Brain Signals into Text

To begin with, AI assists in transforming neural signals into text. The process involves acquiring brain activity readings from individuals, identifying notable electric activity patterns in neurons corresponding specific movements, and using that data to control devices such as robotic arms (Psychology Today, 2021). However, the progression towards transcribing brain signals into text specifically is achieving groundbreaking strides. According to a study by Stanford University, they successfully developed a Brain-Computer Interface (BCI) that transcribes brain signals into text on a screen. This is achieved through recording neural activities through an implanted array of electrodes. Subsequently, pattern recognition and machine learning algorithms pinpoint relevant occurrences in the neural activity and transcribe it into letters. The data is obtained from multiple repetitions of a participant attempting to write different letters (Nature, 2021).

Translating Brain Signals into Audible Speech

On a parallel track, AI is facilitating the conversion of brain signals into audible speech. A noteworthy research contribution hails from the University of California San Francisco (UCSF). The research design involved placing electrodes on the part of participants' brains responsible for motor control. When participants were asked to read 101 sentences aloud, the electrodes recorded brain signals, training an algorithm to reproduce the sound of these spoken words (NewScientist, 2019). This state-of-the-art technology generates audible speech much like a functional vocal tract, relying on signals sent to the lips, jaw, and tongue.

The resulting sound of speech stood up well during trials, as native English speakers correctly transcribed a significant portion of the algorithm-produced sentences. Additionally, the technology held promising potential for direct communication, decoding words from one person’s output for another participant (Nature, 2022). These findings illustrate a promising avenue towards facilitating communication for individuals who have lost the ability to speak due to surgical procedures or motor disorders such as ALS.

Imaging Brain Signals Through AI: An Unexplored Research Frontier

While advancements in converting brain signals into text and audible speech are exciting and demonstrable, the imaging of brain signals using AI remains relatively under-explored. Imaging brain signals using AI could revolutionize neuroscience, making sense of brain signal patterns and providing diagnostic and therapeutic milestones. Regrettably, to date, the available scientific literature is scarce, and concrete milestones remain undefined (AJNR, 2020).

Conclusion

The nexus between AI and neuroscience is proving increasingly beneficial in addressing the challenges of transducing neurological activity into communicable modes. While significant progress has been made in translating brain signals into text and audible speech, much ground remains untouched in the realm of imaging brain signals using AI. The potential within that realm foreshadows unrealized opportunities for understanding brain signals and furthering AI's application in neuroscience. Concrete developments are eagerly anticipated as this frontier promises to provide new diagnostic and therapeutic paradigms.