Many people with ALS experience trouble speaking. To help them stay connected, researchers are developing brain-powered systems to type out words, much like texting people using smart phones. The approach, known as brain-computer interfaces, aims to bypass damaged sections of the central nervous system to allow people with ALS to reach out to family and friends without caregivers’ assistance.
But although many of these investigational devices enable people with paralysis to communicate accurately, the technologies introduced to date are extremely slow for communication purposes. Most recently, a wireless device developed by UMC Utrecht’s Nick Ramsey’s team in the Netherlands enabled a person with ALS to communicate independently but at only 2 letters/minute (see November 2016 news; Vansteensel et al., 2016).
Now, a research team led by Stanford’s Jaimie Henderson and Krishna Shenoy introduce an intracortical brain computer interface (iBCI)-based strategy that enables people with paralysis to communicate up to 8 words (39.2 characters)/minute, more than 4 times faster than existing neural interfaces (Bacher et al., 2015). This is compared to 12-18 words per minute, the average time it takes for able-bodied people to text on their cell phone without word completion assistance (Hoggan et al., 2008; MacKenzie et al., 2009). The technology according to Stanford’s Krishna Shenoy could be adapted to operate digital devices including computers, tablets and smart phones.
The study is published on February 21 in Elife.
The strategy uses decoding algorithms previously developed by Shenoy’s team, to translate brain activity into ‘point and click’ control commands that work much like using a computer mouse (Gilja et al., 2012; Gilja et al., 2015; Kao et al., 2016). The approach, which involves the pre-implantation of electrode arrays in the hand-operating region of the motor cortex, uses a cable to deliver neuronal signals to a computer interface. The device is one of a growing number of neurotechnologies being developed in collaboration with a consortium of neuroscientists, neurosurgeons and bioengineers known as BrainGate that aims to restore independence to people with paralysis in part, by helping them stay connected.
In future, the researchers hope to develop a wireless system, building on previous advances including self-calibration (Jarosiewicz et al., 2015; Borton et al., 2013). In the meantime, the Stanford team plans to combine these technologies with word completion algorithms to increase typing rates. Stay tuned.
Pandarinath C, Nuyujukian P, Blabe CH, Sorice BL, Saab J, Willett FR, Hochberg LR, Shenoy KV, Henderson JM. High performance communication by people with paralysis using an intracortical brain-computer interface. Elife. 2017 Feb 21;6. pii: e18554. [PubMed].
Vansteensel MJ, Pels EG, Bleichner MG, Branco MP, Denison T, Freudenburg ZV, Gosselaar P, Leinders S, Ottens TH, Van Den Boom MA, Van Rijen PC, Aarnoutse EJ, Ramsey NF. Fully Implanted Brain-Computer Interface in a Locked-In Patient with ALS. N Engl J Med. 2016 Nov 24;375(21):2060-2066. [PubMed].
Bacher D, Jarosiewicz B, Masse NY, Stavisky SD, Simeral JD, Newell K, Oakley EM, Cash SS, Friehs G, Hochberg LR. Neural Point-and-Click Communication by a Person With Incomplete Locked-In Syndrome. Neurorehabil Neural Repair. 2015 Jun;29(5):462-71. [PubMed].
Hoggan E, Brewster SA, Johnston J. Investigating the effectiveness of tactile feedback for mobile touchscreens. Twenty-Sixth Annual SIGCHI Conference on Human Factors in Computing Systems (April 5-10, 2008). [Full Text].
MacKenzie IS, Lopez MH, Castelluci S. Text Entry with the Apple iPhone and the Nintendo Wii. Twenty-Seventh Annual SIGCHI Conference on Human Factors in Computing Systems (April 4-9, 2009). [Full Text].
Gilja V, Nuyujukian P, Chestek CA, Cunningham JP, Yu BM, Fan JM, Churchland MM, Kaufman MT, Kao JC, Ryu SI, Shenoy KV. A high-performance neural prosthesis enabled by control algorithm design. Nat Neurosci. 2012 Dec;15(12):1752-7. [PubMed].
Gilja V, Pandarinath C, Blabe CH, Nuyujukian P, Simeral JD, Sarma AA, Sorice BL, Perge JA, Jarosiewicz B, Hochberg LR, Shenoy KV, Henderson JM. Clinical translation of a high-performance neural prosthesis. Nat Med. 2015 Oct;21(10):1142-5. [PubMed].
Kao JC, Nuyujukian P, Ryu SI, Shenoy KV. A high-performance neural prosthesis incorporating discrete state selection with hidden Markov models. IEEE Trans Biomed Eng. 2016 Jun 21. [PubMed].
Jarosiewicz B, Sarma AA, Bacher D, Masse NY, Simeral JD, Sorice B, Oakley EM, Blabe C, Pandarinath C, Gilja V, Cash SS, Eskandar EN, Friehs G, Henderson JM, Shenoy KV, Donoghue JP, Hochberg LR. Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface. Sci Transl Med. 2015 Nov 11;7(313):313ra179. [PubMed].
Borton DA, Yin M, Aceros J, Nurmikko A. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates. J Neural Eng. 2013 Apr;10(2):026010. [PubMed].