Content deleted Content added
cn |
cn |
||
Line 155:
ECoG offers higher spatial resolution, better signal-to-noise ratio, wider frequency range, and less training requirements than scalp-recorded EEG, and at the same time has lower technical difficulty, lower clinical risk, and may have superior long-term stability than intracortical single-neuron recording.<ref>{{cite journal | vauthors = Nicolas-Alonso LF, Gomez-Gil J | title = Brain computer interfaces, a review | journal = Sensors | volume = 12 | issue = 2 | pages = 1211–1279 | date = 2012-01-31 | pmid = 22438708 | pmc = 3304110 | doi = 10.3390/s120201211 | bibcode = 2012Senso..12.1211N | doi-access = free }}</ref> This feature profile and evidence of the high level of control with minimal training requirements shows potential for real world application for people with motor disabilities.<ref name=Mondeofuse>{{cite news | vauthors = Yanagisawa T |title=Electrocorticographic Control of Prosthetic Arm in Paralyzed Patients |doi=10.1002/ana.22613 |quote= ECoG- Based BCI has advantage in signal and durability that are absolutely necessary for clinical application|work=[[American Neurological Association]] |year= 2011 |volume=71 |issue=3 |pages=353–361 }}</ref><ref name=TelepathicCommVowel>{{cite news | vauthors = Pei X |title=Decoding Vowels and Consonants in Spoken and Imagined Words Using Electrocorticographic Signals in Humans |pmid=21750369 |quote= Justin Williams, a biomedical engineer at the university, has already transformed the ECoG implant into a micro device that can be installed with a minimum of fuss. It has been tested in animals for a long period of time – the micro ECoG stays in place and doesn't seem to negatively affect the immune system.|work=J Neural Eng 046028th ser. 8.4 |year=2011 }}</ref>
[[Edward Chang (neurosurgeon)|Edward Chang]] and Joseph Makin from [[UCSF Medical Center|UCSF]] reported that ECoG signals could be used to decode speech from epilepsy patients implanted with high-density ECoG arrays over the peri-Sylvian cortices.<ref>{{cite book | vauthors = Makin JG, Moses DA, Chang EF | title = Brain-Computer Interface Research | veditors = Guger C, Allison BZ, Gunduz A | chapter = Speech Decoding as Machine Translation|date=2021 |pages=23–33 |series=SpringerBriefs in Electrical and Computer Engineering|place=Cham|publisher=Springer International Publishing |language=en |doi=10.1007/978-3-030-79287-9_3 |isbn=978-3-030-79287-9 | s2cid = 239756345 }}</ref><ref>{{cite journal | vauthors = Makin JG, Moses DA, Chang EF | title = Machine translation of cortical activity to text with an encoder-decoder framework | journal = Nature Neuroscience | volume = 23 | issue = 4 | pages = 575–582 | date = April 2020 | pmid = 32231340 | doi = 10.1038/s41593-020-0608-8 | pmc = 10560395 | s2cid = 214704481 }}</ref> They reported word error rates of 3% (a marked improvement from prior efforts) utilizing an encoder-decoder [[neural network]], which translated ECoG data into one of fifty sentences composed of 250 unique words.{{cn}}
====Functional near-infrared spectroscopy====
| |||