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Yes, some AI systems have decoded limited information from brain signals recorded without MRI—but that is not the same as reading any thought on demand. EEG and MEG studies have matched brain activity to speech a person was hearing, and one EEG demonstration decoded words from passages participants silently read. These systems rely on specific tasks, noisy signals, and constrained outputs; they do not establish that a consumer device can transcribe private, unrestricted thoughts.
What “reading thoughts” means in these studies
Brain-decoding AI learns patterns that connect measured brain activity with a particular task or signal. The task might be listening to supplied speech, silently reading supplied text, or attempting to speak. Those are distinct abilities. A system that identifies which of many audio clips a listener heard has not necessarily transcribed that listener’s unprompted inner monologue.
The output also matters: a decoder may select a likely segment, reproduce a rough semantic gist, or generate words that resemble a target passage. These results do not give a system unrestricted access to a person’s subjective mental life.
What MRI-free demonstrations have shown
MEG and EEG while listening to speech
Défossez and colleagues combined four public datasets covering 175 volunteers recorded with EEG or MEG as they listened to short stories and sentences. In their 2023 study, three seconds of MEG data identified the corresponding speech segment with up to 41% accuracy on average across participants, among more than 1,000 possibilities; the best participants reached up to 80%. This was a candidate-matching task—not transcription of arbitrary thoughts. The authors report that predictions relied primarily on lexical and contextual semantic representations, and describe EEG and MEG signals as noisy and variable across people and sessions. Read the study in Nature Machine Intelligence.
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EEG while silently reading
The University of Technology Sydney described DeWave, an EEG system tested with 29 participants who silently read passages. UTS reported a score of about 40% BLEU-1, a text-similarity measure—not “40% of thoughts decoded.” The account notes that the model could substitute semantically related words, such as a general category or synonym instead of the exact noun. The result concerns language tied to a silent-reading task and study protocol, not open-ended transcription of inner speech. See UTS’s description of the demonstration.
What the best-known fMRI decoder required
The 2023 continuous-language decoder that brought widespread attention to “mind reading” used fMRI, not a portable EEG cap. NIH reports that each of three participants listened to 16 hours of spoken stories to train a participant-specific decoder. On new stories, it sometimes reproduced words and phrases, but more often generated text that captured the gist without matching the original wording. The researchers also tested imagined stories and silent-film viewing.
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The decoder did not transfer from one trained participant to another. Successful training required cooperation, and participants could disrupt decoding by focusing on a different task. NIH also notes that the scanner is not portable and the system was not usable outside the laboratory. Alexander Huth, the study’s lead researcher, called it “a real leap forward” for non-invasive decoding, compared with earlier work on single words or short sentences; the achievement remains constrained semantic reconstruction under experimental conditions. Read NIH’s account of the study.
Why the reported accuracy figures cannot be compared directly
The numbers describe different tasks and scoring systems. MEG segment matching asks whether a brain recording corresponds to the right item among more than 1,000 audio candidates. BLEU-1 compares generated text with reference text. Neither number is a general measure of how much of someone’s thoughts an AI can read.
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- Signal method: fMRI uses a scanner; EEG and MEG record brain activity without MRI, but are still research measurement systems. Implanted electrodes are invasive and belong in a separate category.
- Task: Listening to supplied speech, silently reading supplied text, imagining a limited set of stories, and attempting speech are not interchangeable.
- Training and cooperation: Ask how much participant-specific data was used, whether calibration was required, and whether the participant had to cooperate.
- Output and metric: Check whether the result is exact transcription, semantic gist, candidate selection, classification, or text similarity—and what the reported score measures.
- Generalization and control: Look for evidence that a model works with a new person and whether the participant can resist or redirect decoding.
- Purpose and setting: A laboratory proof of concept is not automatically a clinical aid or a consumer product.
Where implanted systems fit
Implanted electrodes can record higher-quality signals and have supported research on speech prostheses for people with paralysis or other disabilities. That work aims to help restore communication; it is not evidence that non-invasive consumer devices can read unrestricted thoughts. A 2024 study of internal speech used intracranial recordings and described its result as a proof of concept for a high-performance internal-speech brain-machine interface. Its findings should be understood in that invasive research context. Read the 2024 internal-speech study.
NIH describes brain-computer interfaces as a potential assistive communication technology and notes that established systems have required invasive surgery. See NIH’s discussion of brain-decoder research and communication applications.
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- Ready to use immediately — get advanced EEG + fNIRS tracking for sleep, focus, and recovery; optional Premium subscription adds AI Coach, deeper brain insights, and access to 500+ meditations.
- Wearable EEG and fNIRS Biofeedback — Put on the soft, adjustable headband and position the sensors to make skin contact. Connect to the Muse app via Bluetooth, select a meditation, sleep or brain training experience, and begin to focus, relax or unwind.
- Safe & Trusted — Built on years of scientific validation, Muse is trusted by neuroscientists, researchers and wellness professionals. Our award-winning SmartSense EEG sensors combined with new fNIRS technology brings the most advanced Muse experience yet.
Can you buy a device that reads general thoughts?
The studies described here do not establish a consumer device that can read general thoughts. An ordinary EEG headset should not be treated as a product that reproduces these laboratory results: the demonstrations depend on defined tasks, particular data and decoding methods, and outputs that are limited or imperfect. A product claim should specify the signal method, exact task, training required, output metric, and evidence that it works beyond a controlled research setting.
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