@article{chen_peng_wu_huang_kim_traylor_muller_chhatbar_nam_feng_et al._2022, title={Numerical and experimental evaluation of low-intensity transcranial focused ultrasound wave propagation using human skulls for brain neuromodulation}, volume={11}, ISSN={["2473-4209"]}, DOI={10.1002/mp.16090}, abstractNote={Abstract}, journal={MEDICAL PHYSICS}, author={Chen, Mengyue and Peng, Chang and Wu, Huaiyu and Huang, Chih-Chung and Kim, Taewon and Traylor, Zachary and Muller, Marie and Chhatbar, Pratik Y. and Nam, Chang S. and Feng, Wuwei and et al.}, year={2022}, month={Nov} }
 @article{mousavian_chen_traylor_greening_2021, title={Depression detection from sMRI and rs-fMRI images using machine learning}, volume={8}, ISSN={["1573-7675"]}, url={http://dx.doi.org/10.1007/s10844-021-00653-w}, DOI={10.1007/s10844-021-00653-w}, journal={JOURNAL OF INTELLIGENT INFORMATION SYSTEMS}, publisher={Springer Science and Business Media LLC}, author={Mousavian, Marzieh and Chen, Jianhua and Traylor, Zachary and Greening, Steven}, year={2021}, month={Aug} }
 @misc{nam_traylor_chen_jiang_feng_chhatbar_2021, title={Direct Communication Between Brains: A Systematic PRISMA Review of Brain-To-Brain Interface}, volume={15}, ISSN={["1662-5218"]}, url={http://dx.doi.org/10.3389/fnbot.2021.656943}, DOI={10.3389/fnbot.2021.656943}, abstractNote={This paper aims to review the current state of brain-to-brain interface (B2BI) technology and its potential. B2BIs function via a brain-computer interface (BCI) to read a sender's brain activity and a computer-brain interface (CBI) to write a pattern to a receiving brain, transmitting information. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to systematically review current literature related to B2BI, resulting in 15 relevant publications. Experimental papers primarily used transcranial magnetic stimulation (tMS) for the CBI portion of their B2BI. Most targeted the visual cortex to produce phosphenes. In terms of study design, 73.3% (11) are unidirectional and 86.7% (13) use only a 1:1 collaboration model (subject to subject). Limitations are apparent, as the CBI method varied greatly between studies indicating no agreed upon neurostimulatory method for transmitting information. Furthermore, only 12.4% (2) studies are more complicated than a 1:1 model and few researchers studied direct bidirectional B2BI. These studies show B2BI can offer advances in human communication and collaboration, but more design and experiments are needed to prove potential. B2BIs may allow rehabilitation therapists to pass information mentally, activating a patient's brain to aid in stroke recovery and adding more complex bidirectionality may allow for increased behavioral synchronization between users. The field is very young, but applications of B2BI technology to neuroergonomics and human factors engineering clearly warrant more research.}, journal={FRONTIERS IN NEUROROBOTICS}, publisher={Frontiers Media SA}, author={Nam, Chang S. and Traylor, Zachary and Chen, Mengyue and Jiang, Xiaoning and Feng, Wuwei and Chhatbar, Pratik Yashvant}, year={2021}, month={May} }
 @article{rakhmatulin_parfenov_traylor_nam_lebedev_2021, title={Low-cost brain computer interface for everyday use}, volume={9}, url={http://dx.doi.org/10.1007/s00221-021-06231-4}, DOI={10.1007/s00221-021-06231-4}, abstractNote={With the growth in electroencephalography (EEG) based applications the demand for affordable consumer solutions is increasing. Here we describe a compact, low-cost EEG device suitable for daily use. The data are transferred from the device to a personal server using the TCP-IP protocol, allowing for wireless operation and a decent range of motion for the user. The device is compact, having a circular shape with a radius of only 25 mm, which would allow for comfortable daily use during both daytime and nighttime. Our solution is also very cost effective, approximately $350 for 24 electrodes. The built-in noise suppression capability improves the accuracy of recordings with a peak input noise below 0.35 μV. Here, we provide the results of the tests for the developed device. On our GitHub page, we provide detailed specification of the steps involved in building this EEG device which should be helpful to readers designing similar devices for their needs  https://github.com/Ildaron/ironbci .}, journal={Experimental Brain Research}, publisher={Springer Science and Business Media LLC}, author={Rakhmatulin, Ildar and Parfenov, Andrey and Traylor, Zachary and Nam, Chang S. and Lebedev, Mikhail}, year={2021}, month={Sep} }