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Scientists use AI neural network to translate speech from brain activity

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7 years ago

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Three recently published studies focused on using artificial intelligence (AI) neural networks to generate audio output from brain signals have shown promising results, namely by producing identifiable sounds up to 80% of the time. Participants in the studies first had their brain signals measured while they were either reading aloud or listening to specific words. All the data was then given to a neural network to “learn” how to interpret brain signals after which the final sounds were reconstructed for listeners to identify. These results represent hopeful prospects for the field of brain-computer interfaces (BCIs), where thought-based communication is quickly moving from the realm of science fiction to reality.

The idea of connecting human brains to computers is far from new. In fact, several relevant milestones have been made in recent years including enabling paralyzed individuals to operate tablet computers with their brain waves. Elon Musk has also famously brought attention to the field with Neuralink, his BCI company that essentially hopes to merge human consciousness with the power of the Internet. As brain-computer interface technology expands and develops new ways to foster communication between brains and machines, studies like these, originally highlighted by Science Magazine, will continue demonstrating the steady march of progress.

Functional areas of the human brain. | Credit: Blausen.com staff (2014) via CC BY 3.0.

In the first study conducted by researchers from Columbia University and Hofstra Northwell School of Medicine, both in New York, five epileptic participants had the brain signals from their auditory cortexes recorded as they listened to stories and numbers being read to them. The signal data was provided to a neural network for analysis which then reconstructed audio files that were accurately identified by participating listeners 75% of the time.

In the second study conducted by a team from the University of Bremen (Germany), Maastricht University (Netherlands), Northwestern University (Illinois), and Virginia Commonwealth University (Virginia), brain signal data was gathered from six patients’ speech planning and motor areas while undergoing tumor surgeries. Each patient read specific words aloud to target the data collected. After the brain data and audio data were given to their neural network for training, the program was given brain signals not included in the training set to recreate audio, the result producing words that were recognizable 40% of the time.

Finally, in a third study by a team at the University of California, San Francisco, three participants with epilepsy read text aloud while brain activity was captured from the speech and motor areas of their brains. The audio generated from their neural network’s analysis of the signal readings was presented to a group of 166 people who were asked to identify the sentences from a multiple choice test – some sentences were identified with 80% accuracy.

While the research presented in these studies shows serious progress towards connecting human brains to computers, there are still a few significant hurdles. For one, the way neuron signal patterns in the brain translate into sounds varies from person to person, so neural networks must be trained on each individual person. The best results require the best data possible, i.e., the most precise neuron signals possible, meaning this is something that can only be obtained by placing electrodes in the brain itself. The opportunities to collect data at this invasive level for research are limited, relying on voluntary participation and approval of experiments.

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All three of the studies highlighted demonstrated an ability to reconstruct speech based on neural data in some significant capacity; however, also in all cases, the study participants were able to create audible sounds to use with the computer training set. In the case of patients unable to speak, the level of difficultly in interpreting the brain’s speech signals from other signals will be the biggest challenge. Also, the differences between brain signals during actual speech vs. thinking about speech will complicate matters further.

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Accidental computer geek, fascinated by most history and the multiplanetary future on its way. Quite keen on the democratization of space. | It's pronounced day-sha, but I answer to almost any variation thereof.

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Elon Musk

Elon Musk teases previously unknown Tesla Optimus capability

Elon Musk revealed over the weekend that the humanoid robot should be able to utilize Tesla’s dataset for Full Self-Driving (FSD) to operate cars not manufactured by Tesla.

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2 hours ago

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October 6, 2025

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Credit: @heydave7/X

Elon Musk revealed a new capability that Tesla Optimus should have, and it is one that will surely surprise many people, as it falls outside the CEO’s scope of his several companies.

Tesla Optimus is likely going to be the biggest product the company ever develops, and Musk has even predicted that it could make up about 80 percent of the company’s value in the coming years.

Teasing the potential to eliminate any trivial and monotonous tasks from human life, Optimus surely has its appeal.

However, Musk revealed over the weekend that the humanoid robot should be able to utilize Tesla’s dataset for Full Self-Driving (FSD) to operate cars not manufactured by Tesla:

FSD would essentially translate from operation in Tesla vehicles from a driverless perspective to Optimus, allowing FSD to basically be present in any vehicle ever made. Optimus could be similar to a personal chauffeur, as well as an assistant.

Optimus has significant hype behind it, as Tesla has been meticulously refining its capabilities. Along with Musk’s and other executives’ comments about its potential, it’s clear that there is genuine excitement internally.

This past weekend, the company continued to stoke hype behind Optimus by showing a new video of the humanoid robot learning Kung Fu and training with a teacher:

Tesla plans to launch its Gen 3 version of Optimus in the coming months, and although we saw a new-look robot just last month, thanks to a video from Salesforce CEO and Musk’s friend Marc Benioff, we have been told that this was not a look at the company’s new iteration.

Instead, Gen 3’s true design remains a mystery for the general public, but with the improvements between the first two iterations already displayed, we are sure the newest version will be something special.

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Investor's Corner

Cantor Fitzgerald reaffirms bullish view on Tesla after record Q3 deliveries

The firm reiterated its Overweight rating and $355 price target.

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4 hours ago

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October 6, 2025

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(Credit: Tesla)

Cantor Fitzgerald is maintaining its bullish outlook on Tesla (NASDAQ:TSLA) following the company’s record-breaking third quarter of 2025. 

The firm reiterated its Overweight rating and $355 price target, citing strong delivery results driven by a rush of consumer purchases ahead of the end of the federal tax credit on September 30.

On Tesla’s vehicle deliveries in Q3 2025

During the third quarter of 2025, Tesla delivered a total of 497,099 vehicles, significantly beating analyst expectations of 443,079 vehicles. As per Cantor Fitzgerald, this was likely affected by customers rushing at the end of Q3 to purchase an EV due to the end of the federal tax credit, as noted in an Investing.com report. 

“On 10/2, TSLA pre-announced that it delivered 497,099 vehicles in 3Q25 (its highest quarterly delivery in company history), significantly above Company consensus of 443,079, and above 384,122 in 2Q25. This was due primarily to a ‘push forward effect’ from consumers who rushed to purchase or lease EVs ahead of the $7,500 EV tax credit expiring on 9/30,” the firm wrote in its note.

A bright spot in Tesla Energy

Cantor Fitzgerald also highlighted that while Tesla’s full-year production and deliveries would likely fall short of 2024’s 1.8 million total, Tesla’s energy storage business remains a bright spot in the company’s results.

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“Tesla also announced that it had deployed 12.5 GWh of energy storage products in 3Q25, its highest in company history vs. our estimate/Visible Alpha consensus of 11.5/10.9 GWh (and vs. ~6.9 GWh in 3Q24). Tesla’s Energy Storage has now deployed more products YTD than all of last year, which is encouraging. We expect Energy Storage revenue to surpass $12B this year, and to account for ~15% of total revenue,” the firm stated. 

Tesla’s strong Q3 results have helped lift its market capitalization to $1.47 trillion as of writing. The company also teased a new product reveal on X set for October 7, which the firm stated could serve as another near-term catalyst.

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Elon Musk

Elon Musk’s xAI becomes Memphis’ 2nd largest taxpayer in just one year: report

Elon Musk’s artificial intelligence startup, xAI, is reshaping Memphis’s economic landscape.

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4 hours ago

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October 6, 2025

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Credit: xAI

Elon Musk’s artificial intelligence startup, xAI, is reshaping Memphis’s economic landscape. In just twelve months, the company has become the city and county’s second largest taxpayer.

The update was related in a report from The Wall Street Journal.

Memphis’ second-largest taxpayer

xAI is currently transforming a defunct Mississippi power plant into a crucial hub for AI, supplying electricity to its Colossus supercomputer cluster and its successor, Colossus 2. Together, the Colossi supercomputers will host more than half a million Nvidia chips that would be used for the development and improvement of Grok, xAI’s large language model. 

The buildout has injected billions into the region, making xAI one of Memphis’s most significant private investors and a symbol of the city’s high-tech aspirations. Bill Dunavant III, a Memphis businessman who sits on the board of directors of the city’s chamber of commerce, highlighted xAI’s contribution to the city’s economy in a comment to the WSJ. 

“In one year, xAI has become the second largest taxpayer in the city and county after FedEx,” he said. A spokesman for the Greater Memphis Chamber of Commerce has also stated that xAI has demonstrated “substantial economic commitment to our region, without any tax incentives.”

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Not without controversy

Despite the economic boost, xAI’s footprint has drawn scrutiny. The company’s natural-gas-powered turbines are expected to consume a substantial amount of water and electricity. Critics have also expressed worries about pollution and increased utility costs, though others see Musk’s wastewater recycling plans and cleanup initiatives as meaningful offsets.

As per the WSJ, xAI’s positioning in the market may be quite different than what Musk is typically used to, considering that the CEO tends to become a first mover in key industries, such as the EV segment with Tesla and private spaceflight with SpaceX. With xAI, however, he is catching up to competitors, the most notable of which is a company he co-founded, OpenAI, and its ubiquitous large language model, ChatGPT.

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