@article {3419,
	title = {Brain-to-text: Decoding spoken sentences from phone representations in the brain.},
	journal = {Journal of Neural Engineering},
	year = {2015},
	month = {06/2015},
	abstract = {It 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.},
	keywords = {automatic speech recognition, brain-computer interface, broadband gamma, ECoG, Electrocorticography, pattern recognition, speech decoding, speech production},
	doi = {10.3389/fnins.2015.00217},
	url = {http://journal.frontiersin.org/article/10.3389/fnins.2015.00217/abstract},
	author = {Herff, C. and Heger, D. and Pesters, Adriana de and Telaar, D. and Peter Brunner and Gerwin Schalk and Schultz, T.}
}
@article {3358,
	title = {Decoding spectrotemporal features of overt and covert speech from the human cortex.},
	journal = {Frontiers in Neuroengineering},
	volume = {7},
	year = {2014},
	month = {03/2014},
	abstract = {Auditory 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{\textendash}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.},
	keywords = {covert speech, decoding model, Electrocorticography, pattern recognition, speech production},
	doi = {10.3389/fneng.2014.00014},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/24904404},
	author = {Martin, St{\'e}phanie and Peter Brunner and Holdgraf, Chris and Heinze, Hans-Jochen and Nathan E. Crone and Rieger, Jochem and Gerwin Schalk and Robert T. Knight and Pasley, Brian N.}
}
