02272nas a2200277   4500008004100000245008600041210006900127260001200196520140600208653003301614653002901647653002001676653000901696653002501705653002401730653002001754653002201774100001401796700001401810700002401824700001501848700001901863700001901882700001601901856007701917       2015                        eng d00aBrain-to-text: Decoding spoken sentences from phone representations in the brain.0 aBraintotext Decoding spoken sentences from phone representations  c06/20153 aIt has long been speculated whether communication between humans and machines based on natural speech related cortical activity is possible. Over the past decade, studies have suggested that it is feasible to recognize isolated aspects of speech from neural signals, such as auditory features, phones or one of a few isolated words. However, until now it remained an unsolved challenge to decode continuously spoken speech from the neural substrate associated with speech and language processing. Here, we show for the first time that continuously spoken speech can be decoded into the expressed words from intracranial electrocorticographic (ECoG) recordings.Specifically, we implemented a system, which we call Brain-To-Text that models single phones, employs techniques from automatic speech recognition (ASR), and thereby transforms brain activity while speaking into the corresponding textual representation. Our results demonstrate that our system can achieve word error rates as low as 25% and phone error rates below 50%. Additionally, our approach contributes to the current understanding of the neural basis of continuous speech production by identifying those cortical regions that hold substantial information about individual phones. In conclusion, the Brain-To- Text system described in this paper represents an important step toward human-machine communication based on imagined speech.10aautomatic speech recognition10abrain-computer interface10abroadband gamma10aECoG10aElectrocorticography10apattern recognition10aspeech decoding10aspeech production1 aHerff, C.1 aHeger, D.1 ade Pesters, Adriana1 aTelaar, D.1 aBrunner, Peter1 aSchalk, Gerwin1 aSchultz, T.  uhttp://journal.frontiersin.org/article/10.3389/fnins.2015.00217/abstract02900nas a2200277   4500008004100000245008800041210006900129260001200198490000600210520205900216653001802275653001902293653002502312653002402337653002202361100002302383700001902406700002002425700002402445700002202469700001902491700001902510700002302529700002202552856004802574       2014                        eng d00aDecoding spectrotemporal features of overt and covert speech from the human cortex.0 aDecoding spectrotemporal features of overt and covert speech fro  c03/20140 v73 aAuditory perception and auditory imagery have been shown to activate overlapping brain regions. We hypothesized that these phenomena also share a common underlying neural representation. To assess this, we used electrocorticography intracranial recordings from epileptic patients performing an out loud or a silent reading task. In these tasks, short stories scrolled across a video screen in two conditions: subjects read the same stories both aloud (overt) and silently (covert). In a control condition the subject remained in a resting state. We first built a high gamma (70–150 Hz) neural decoding model to reconstruct spectrotemporal auditory features of self-generated overt speech. We then evaluated whether this same model could reconstruct auditory speech features in the covert speech condition. Two speech models were tested: a spectrogram and a modulation-based feature space. For the overt condition, reconstruction accuracy was evaluated as the correlation between original and predicted speech features, and was significant in each subject (p < 0.00001; paired two-sample t-test). For the covert speech condition, dynamic time warping was first used to realign the covert speech reconstruction with the corresponding original speech from the overt condition. Reconstruction accuracy was then evaluated as the correlation between original and reconstructed speech features. Covert reconstruction accuracy was compared to the accuracy obtained from reconstructions in the baseline control condition. Reconstruction accuracy for the covert condition was significantly better than for the control condition (p < 0.005; paired two-sample t-test). The superior temporal gyrus, pre- and post-central gyrus provided the highest reconstruction information. The relationship between overt and covert speech reconstruction depended on anatomy. These results provide evidence that auditory representations of covert speech can be reconstructed from models that are built from an overt speech data set, supporting a partially shared neural substrate.10acovert speech10adecoding model10aElectrocorticography10apattern recognition10aspeech production1 aMartin, Stéphanie1 aBrunner, Peter1 aHoldgraf, Chris1 aHeinze, Hans-Jochen1 aCrone, Nathan, E.1 aRieger, Jochem1 aSchalk, Gerwin1 aKnight, Robert, T.1 aPasley, Brian, N.  uhttp://www.ncbi.nlm.nih.gov/pubmed/24904404