01911nas a2200349   4500008004100000022001400041245004100055210004000096260001600136300000900152490000700161520102100168653001001189653001001199653002001209653002001229653001101249653001101260653000901271653001801280653001601298653001601314100001301330700001901343700001601362700002201378700002501400700001901425700001901444700001701463856008101480       2024                        eng d  a2041-172300aGraded decisions in the human brain.0 aGraded decisions in the human brain  c2024 May 21  a43080 v153 a<p>Decision-makers objectively commit to a definitive choice, yet at the subjective level, human decisions appear to be associated with a degree of uncertainty. Whether decisions are definitive (i.e., concluding in all-or-none choices), or whether the underlying representations are graded, remains unclear. To answer this question, we recorded intracranial neural signals directly from the brain while human subjects made perceptual decisions. The recordings revealed that broadband gamma activity reflecting each individual's decision-making process, ramped up gradually while being graded by the accumulated decision evidence. Crucially, this grading effect persisted throughout the decision process without ever reaching a definite bound at the time of choice. This effect was most prominent in the parietal cortex, a brain region traditionally implicated in decision-making. These results provide neural evidence for a graded decision process in humans and an analog framework for flexible choice behavior.</p>10aAdult10aBrain10aChoice Behavior10aDecision Making10aFemale10aHumans10aMale10aParietal Lobe10aUncertainty10aYoung Adult1 aXie, Tao1 aAdamek, Markus1 aCho, Hohyun1 aAdamo, Matthew, A1 aRitaccio, Anthony, L1 aWillie, Jon, T1 aBrunner, Peter1 aKubanek, Jan  uhttps://neurotechcenter.org/publications/2024/graded-decisions-human-brain-002336nas a2200229   4500008004100000022001400041245012600055210006900181260001600250490000700266520152600273653001001799653002501809653002701834653001101861653004101872100001601913700001901929700001901948700001901967856012001986       2024                        eng d  a2050-084X00aNovel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics.0 aNovel cyclic homogeneous oscillation detection method for high a  c2024 Sep 060 v123 a<p>Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/ noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation's fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.</p>10aBrain10aElectrocorticography10aElectroencephalography10aHumans10aSignal Processing, Computer-Assisted1 aCho, Hohyun1 aAdamek, Markus1 aWillie, Jon, T1 aBrunner, Peter  uhttps://neurotechcenter.org/publications/2024/novel-cyclic-homogeneous-oscillation-detection-method-high-accuracy-002500nas a2200385   4500008004100000022001400041245010500055210006900160260001600229300000900245490000700254520128700261653003001548653001701578653002701595653001101622653002501633653001801658653004601676653001901722100002201741700001901763700001701782700001301799700001901812700001601831700002201847700001801869700002301887700002101910700001901931700002401950700002301974856011701997       2024                        eng d  a2045-232200aA novel theta-controlled vibrotactile brain-computer interface to treat chronic pain: a pilot study.0 anovel thetacontrolled vibrotactile braincomputer interface to tr  c2024 Feb 10  a34330 v143 a<p>Limitations in chronic pain therapies necessitate novel interventions that are effective, accessible, and safe. Brain-computer interfaces (BCIs) provide a promising modality for targeting neuropathology underlying chronic pain by converting recorded neural activity into perceivable outputs. Recent evidence suggests that increased frontal theta power (4-7 Hz) reflects pain relief from chronic and acute pain. Further studies have suggested that vibrotactile stimulation decreases pain intensity in experimental and clinical models. This longitudinal, non-randomized, open-label pilot study's objective was to reinforce frontal theta activity in six patients with chronic upper extremity pain using a novel vibrotactile neurofeedback BCI system. Patients increased their BCI performance, reflecting thought-driven control of neurofeedback, and showed a significant decrease in pain severity (1.29 ± 0.25 MAD, p = 0.03, q = 0.05) and pain interference (1.79 ± 1.10 MAD p = 0.03, q = 0.05) scores without any adverse events. Pain relief significantly correlated with frontal theta modulation. These findings highlight the potential of BCI-mediated cortico-sensory coupling of frontal theta with vibrotactile stimulation for alleviating chronic pain.</p>10abrain-computer interfaces10aChronic Pain10aElectroencephalography10aHumans10aLongitudinal Studies10aNeurofeedback10aNon-Randomized Controlled Trials as Topic10aPilot Projects1 aDemarest, Phillip1 aRustamov, Nabi1 aSwift, James1 aXie, Tao1 aAdamek, Markus1 aCho, Hohyun1 aWilson, Elizabeth1 aHan, Zhuangyu1 aBelsten, Alexander1 aLuczak, Nicholas1 aBrunner, Peter1 aHaroutounian, Simon1 aLeuthardt, Eric, C  uhttps://neurotechcenter.org/publications/2024/novel-theta-controlled-vibrotactile-brain-computer-interface-treat