Human speech network, Brain computer interfaces

1. Neural mechanisms of human speech processing

The speech processing mechanisms (localization, connectivity and temporal dynamics) are investigated. Recently, we discovered a cooperative cortical network for categorical processing of lexical tone, the most important building block of tonal language(Guo et al, 2020), and revealed the surprising role of the motor cortex in the process (Si et al., 2017). In the other direction, to improve the accuracy of pre-surgical mapping of language and motor functions and better outcome of neurosurgical patients, we developed methods of parcellating the cortical functional networks (Qian 2013; Wang, 2015)

2. Minimally invasive BCI for communication

Based on our findings of neural encoding mechanisms and networks of the human brain, we invented a new BCI protocol and device which provides paralyzed patients with reliable and low-risk BCI communication, like typing and speaking with brain signals. My lab developed the first BCI typing protocol using visual motion response over human MT, and a novel design of minimally invasive BCI using intracranial EEG (Zhang 2013). With collaboration from neurosurgeons, pre-clinical trials of implanted BCI have been carried out for paralyzed patients (Yan 2020). To reduce the power consumption of BCI implants, we are also working with Tsinghua microelectronics team, to explore a new design of the neural signal processing system by using memristor arrays (Liu 2020).

New Century Excellent Talents in University (Ministry of Education, China)

1. Guo N, Si X, Zhang Y, Ding Y, Zhou W, Zhang D, Hong B*. Speech frequency-following response in human auditory cortex is more than a simple tracking. Neuroimage. 2020 Nov 11;226:117545. doi:10.1016/j.neuroimage.2020.117545.

2. Yan Y, Dahmani L, Ren J, Shen L, Peng X, Wang R, He C, Jiang C, Gong C, Tian Y, Zhang J, Guo Y, Lin Y, Li S, Wang M, Li L, Hong B*, Liu H*. Reconstructing lost BOLD signal in individual participants using deep machine learning. Nature Communications. 2020 Oct 7;11(1):5046. doi: 10.1038/s41467-020-18823-9.

3. Liu Z, Tang J, Gao B, Yao P, Li X, Liu D, Zhou Y, Qian H, Hong B*, Wu H*. Neural signal analysis with memristor arrays towards high-efficiency brain-machine interfaces. Nature Communications. 2020 Aug 25;11(1):4234. doi: 10.1038/s41467-020-18105-4.

4. Yan Y, Qian T, Xu X, Han H, Ling Z, Zhou W, Liu H, Hong B*. Human cortical networking by probabilistic and frequency-specific coupling. Neuroimage. 2020 Feb 15;207:116363. doi: 10.1016/j.neuroimage.2019.116363.

5. Si X, Zhou W, Hong B*. Cooperative cortical network for categorical processing of Chinese lexical tone. PNAS. 114(46):12303-12308. 2017

6. Wang D, Buckner RL, Fox MD, Holt DJ, Holmes AJ, Stoecklein S, Langs G, Pan R, Qian T, Li K, Baker JT, Stufflebean SM, Wang K, Wang X, Hong B*, Liu H*. Parcellating cortical functional networks in individuals. Nature neuroscience, 18(12), 1853–1860. 2015

7. Gao S*, Wang Y, Gao X, & Hong B.. Visual and auditory brain–computer interfaces. IEEE Transactions on Biomedical Engineering, 61(5), 1436-1447. 2014

8. Zhang D, Song H Xu R, Zhou W, Ling Z*, Hong B*. Toward a minimally invasive brain-computer interface using a single subdural channel: A visual speller study. NeuroImage 71:30-41, 2013

9. Qian T, Zhou W, Ling Z, Gao S, Liu H, Hong B*. Fast presurgical functional mapping using task-related intracranial high gamma activity. Journal of Neurosurgery, 119(1):26-36,2013 (with Editorial)