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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesSpeech recognition turns spoken audio into text; brain-to-text decoding turns recorded neural activity associated with speech into words or other communication outputs. Both can use machine learning and language models, but they start with different signals and have been demonstrated in very different settings. Brain-to-text is not a routine way to read arbitrary thoughts.
What each technology takes as input
Speech recognition starts with audio
Automatic speech recognition (ASR) accepts speech as an audio signal—captured by a microphone or supplied in an audio file—and estimates what was said. NIST defines ASR as technology that “accepts speech as input and determines what was spoken.” NIST’s glossary entry was added June 12, 2023.
Brain-to-text starts with neural recordings
Brain-to-text systems record neural activity associated with intended or attempted speech, then use a decoder to estimate linguistic units or words. Depending on the system, intermediate steps may represent phones or phonemes, with a vocabulary and language model helping produce text. Speech neuroprostheses can also turn neural activity into audible speech or orofacial movement, as described in a review of the field.
How the technologies overlap—and how they do not
The distinction is not “AI versus no AI.” Both technologies can use machine learning, phoneme representations, decoding algorithms and language models. The key difference is the source signal: ASR processes audio, while brain-to-text systems process neural recordings.
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The 2015 Brain-To-Text study used intracranial electrocorticography (ECoG) and modeled individual phones, borrowing methods from speech recognition to convert neural activity recorded during speaking into text. A 2023 speech neuroprosthesis decoded neural activity into phoneme probabilities and combined them with a language model. These approaches share elements of their decoding pipelines, but their inputs and experimental contexts remain distinct.
What the research demonstrations show
Reported results depend on the participant, recording method, task, vocabulary and error metric. The figures below come from different studies and should not be treated as a head-to-head ranking.
Rank #2
| Study and setup | Reported result | What the result describes |
|---|---|---|
| Brain-To-Text, Frontiers in Neuroscience (2015) | 25% word error rate at best | An early system using intracranial ECoG; not a current benchmark for the whole field. |
| Speech neuroprosthesis, Nature (2023) | 62 words per minute; 9.1% word error rate with a 50-word vocabulary and 23.8% with a 125,000-word vocabulary | One participant with ALS using an intracortical system to decode attempted speech. Results are specific to that participant and setup, not a product guarantee. |
| Noninvasive decoding, Nature Neuroscience (2026) | Mean character error rate of 29% with MEG and 65% with EEG | Thirty-five healthy volunteers typed briefly memorized sentences. This differs from attempted-speech tasks and uses character error rate rather than word error rate. |
Because these studies used different signals, tasks, populations and metrics, their numbers cannot be compared as if they measured the same capability. In particular, a word error rate from attempted speech and a character error rate from typed memorized sentences answer different questions.
Can a computer read thoughts?
That is not what these demonstrations establish. In the invasive examples, researchers decoded neural activity associated with attempted speech in specific participants and controlled study settings. The 2026 noninvasive study decoded sentences while healthy volunteers typed briefly memorized text; it does not demonstrate unrestricted thought or speech decoding.
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A 2025 NIH summary describes research involving attempted and imagined speech in four participants and reports exploration of safeguards against unintentional inner-speech output. Those findings make user control and the ability to communicate intentionally important design considerations, not evidence that ordinary devices can passively read private thoughts. NIH’s summary provides the study context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does brain-to-text require surgery?
No single recording method defines the entire field. Some speech neuroprostheses use implanted electrodes, including the intracortical system in the 2023 study. Noninvasive methods such as MEG and EEG have also been tested, including the 2026 typed-sentence study. But a noninvasive recording method does not by itself show that a system can support everyday, unrestricted communication; the task and participants matter.
Rank #4
For context on an earlier assistive-device demonstration, NIH reported in 2021 on a system that translated brain signals into words displayed on a screen. That report describes a specific device and participant, rather than a general-purpose consumer technology. Read the NIH account.
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What to keep in mind when comparing claims
- Check the input: ASR needs audio; brain-to-text needs neural recordings.
- Check how signals were recorded: a microphone, implanted electrodes, ECoG, MEG and EEG are not interchangeable setups.
- Check the task: spoken audio, attempted speech, imagined speech and typing memorized sentences test different abilities.
- Check the metric and vocabulary: word error rate, character error rate, speed and vocabulary size describe different aspects of performance.
- Check who took part: results from a small or specific clinical cohort do not establish performance for other users.
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