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  "title": "Decode inner speech from brain signals",
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  "summary": "Independent review by claude-independent-prior-art-reviewer-20261106: Inner speech decoding works in a handful of implanted participants and nowhere else, so the Main KPI of 8,000,000,000 people able to use speech decoding is open work. The strongest result is Kunz et al. (Cell 2025): in four participants with intracortical motor-cortex arrays, inner speech is robustly represented, imagined sentences were decoded in real time, and a separable motor-intent dimension plus deliberate safeguards were needed to stop a speech neuroprosthesis decoding private inner speech (https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=%22inner%20speech%22%20AND%20%22intracortical%22%20AND%20neuroprosthesis&format=json&pageSize=5&resultType=core). Non-invasive decoding does not generalise: EEG inner-speech studies report 90 to 98 percent within-subject accuracy on datasets of 5 to 10 participants but only 52 to 64 percent cross-subject, and a functional-connectivity study of 10 participants on four Spanish words reached 43.7 percent. A 2026 systematic review puts invasive median multiclass accuracy at 77.7 percent and notes only six studies validated results in paralysed people, all invasive. Open source is hobby-scale: 15 repositories, the largest with 6 stars (https://api.github.com/search/repositories?q=speech+decoding+brain+computer+interface&per_page=10&sort=stars). Honest limits: intracortical inner-speech decoding is published prior art, so replicating it is a known answer; the open work is non-invasive, cross-subject, open-vocabulary decoding, and the consent and privacy machinery a universal KPI would demand, which the Cell paper raises but does not solve. (open solution found: no; known answer: no; 6 project-level searches of api.github.com/search/repositories, export.arxiv.org/api/query, api.crossref.org/works; draft 79ec7f46b6fb14c4d6f4aaf7482c3a4c7a3be5c4e502bfa7ee044ac8db3de8e0, source d588568e8761a3847c47af00c97887717c09208a53d861e38ae30a600141a6d0)",
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    "basis": "Independent review by claude-independent-prior-art-reviewer-20261106: Inner speech decoding works in a handful of implanted participants and nowhere else, so the Main KPI of 8,000,000,000 people able to use speech decoding is open work. The strongest result is Kunz et al. (Cell 2025): in four participants with intracortical motor-cortex arrays, inner speech is robustly represented, imagined sentences were decoded in real time, and a separable motor-intent dimension plus deliberate safeguards were needed to stop a speech neuroprosthesis decoding private inner speech (https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=%22inner%20speech%22%20AND%20%22intracortical%22%20AND%20neuroprosthesis&format=json&pageSize=5&resultType=core). Non-invasive decoding does not generalise: EEG inner-speech studies report 90 to 98 percent within-subject accuracy on datasets of 5 to 10 participants but only 52 to 64 percent cross-subject, and a functional-connectivity study of 10 participants on four Spanish words reached 43.7 percent. A 2026 systematic review puts invasive median multiclass accuracy at 77.7 percent and notes only six studies validated results in paralysed people, all invasive. Open source is hobby-scale: 15 repositories, the largest with 6 stars (https://api.github.com/search/repositories?q=speech+decoding+brain+computer+interface&per_page=10&sort=stars). Honest limits: intracortical inner-speech decoding is published prior art, so replicating it is a known answer; the open work is non-invasive, cross-subject, open-vocabulary decoding, and the consent and privacy machinery a universal KPI would demand, which the Cell paper raises but does not solve. (open solution found: no; known answer: no; 6 project-level searches of api.github.com/search/repositories, export.arxiv.org/api/query, api.crossref.org/works; draft 79ec7f46b6fb14c4d6f4aaf7482c3a4c7a3be5c4e502bfa7ee044ac8db3de8e0, source d588568e8761a3847c47af00c97887717c09208a53d861e38ae30a600141a6d0)",
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      "export.arxiv.org/api/query \"decode inner speech brain signals\" url=https://export.arxiv.org/api/query?search_query=all%3Adecode%20AND%20all%3Ainner%20AND%20all%3Aspeech%20AND%20all%3Abrain%20AND%20all%3Asignals&start=0&max_results=10 sha256=4a187c63a3b4bc693d2243b5c0d7767214069ad712ae55c913d1c99a0b92c9de",
      "api.crossref.org/works \"decode inner speech brain signals\" url=https://api.crossref.org/works?query=decode%20inner%20speech%20brain%20signals&rows=10&select=DOI,title sha256=b5d83a65971c0e3a4f06536b02d5275aab26e96b4e1d014d6d4009b1cee276b4",
      "api.github.com/search/repositories \"decode inner speech neuroscience mental health\" url=https://api.github.com/search/repositories?q=decode%20inner%20speech%20neuroscience%20mental%20health&per_page=10 sha256=4af480b8ee5b87b369a76c49bd22c9a783908272ebffbe97898f8ab0f0772a5f",
      "export.arxiv.org/api/query \"decode inner speech neuroscience mental health\" url=https://export.arxiv.org/api/query?search_query=all%3Adecode%20AND%20all%3Ainner%20AND%20all%3Aspeech%20AND%20all%3Aneuroscience%20AND%20all%3Amental%20AND%20all%3Ahealth&start=0&max_results=10 sha256=684155267c0363a8cd298c91ae71edf29e0cb96c8d45c1e0a237e9ee129192b7",
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