Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to control external devices. This paper presents the decoding of intuitive upper extremity imagery for multi-directional arm reaching tasks in three-dimensional (3D) environments. We designed and implemented an experimental environment in which electroencephalogram (EEG) signals can be acquired for movement execution and imagery. Fifteen subjects participated in our experiments. We proposed a multi-directional convolution neural network-bidirectional long short-term memory network (MDCBN)-based deep learning framework. The decoding performances for six directions in 3D space were measured by the correlation coefficient (CC) and the normalized root mean square error (NRMSE) between predicted and baseline velocity profiles. The grand-averaged CCs of multi-direction were 0.47 and 0.45 for the execution and imagery sessions, respectively, across all subjects. The NRMSE values were below 0.2 for both sessions. Furthermore, in this study, the proposed MDCBN was evaluated by two online experiments for real-time robotic arm control, and the grand-averaged success rates were approximately 0.60 (±0.14) and 0.43 (±0.09), respectively. Hence, we demonstrate the feasibility of intuitive robotic arm control based on EEG signals for real-world environments.

Kim H, et al. J Clin Neurol. 2022 Mar;18(2):140-151. https://doi.org/10.3988/jcn.2022.18.2.140

The Monro-Kellie doctrine describes the principle of homeostatic intracerebral volume regulation, which stipulates that the total volume of the parenchyma, cerebrospinal fluid, and blood remains constant. Hypothetically, a slow shift (e.g., brain edema development) in the irregular vasomotion-driven exchanges of these compartmental volumes may lead to increased intracranial hypertension. To evaluate this paradigm in a clinical setting and measure the processes involved in the regulation of systemic intracranial volume, we quantified cerebral blood flow velocity (CBFv) in the middle cerebral artery, arterial blood pressure (ABP), and intracranial pressure (ICP), in 238 brain-injured subjects. Relative changes in compartmental compliances C _a (arterial) and C _i (combined venous and CSF compartments) were mathematically estimated using these raw signals through time series analysis; C _a and C _i were used to compute an index of cerebral compliance (ICC) as a moving correlation coefficient between C _a and C _i . Conceptually, a negative ICC would represent a functional Monro-Kellie doctrine by illustrating volumetric compensations between C _a and C _i . Clinical observations show that Lundberg A-waves and arterial hypertension were associated with negative ICC, whereas in refractory intracranial hypertension, a positive ICC was observed. In subjects who died, ICC was significantly greater than in survivors (0.46±0.027 versus 0.22±0.017; p <0.01) over the first 5 days of intensive care. The mortality rate is 5% when ICC is less than 0, and 43% when above 0.7. ICC above 0.7 was associated with terminally elevated ICP (chi-square p =0.026). We propose that the Monro-Kellie doctrine can be monitored in real time to illustrate the state of intracranial volume regulation.

Arterial blood pressure (ABP) monitoring may permit the early diagnosis and management of cardiovascular disease (CVD); however, existing methods for measuring ABP outside the clinic use inconvenient cuff sphygmomanometry, or do not estimate continuous ABP waveforms. This study proposes a novel deep learning model DeepCNAP for estimating continuous BP waveform from a noninvasively measured photoplethysmography (PPG) signal in real-time. DeepCNAP was designed through the combination of deep convolutional networks and self-attention. The proposed method was constructed via 10-fold cross-validation based on the MIMIC database (the number of subjects = 942, recording time = 374.43 hours). The performance of DeepCNAP was evaluated from two perspectives: estimating ABP from PPG and classifying hemodynamically unstable events (i.e., hypertension, prehypertension, hypotension, and the normal state). The mean absolute errors of the BP estimates were 3.40 ± 4.36 mmHg for systolic BP, 1.75 ± 2.25 mmHg for diastolic BP, and 3.23 ± 2.21 mmHg for the BP waveform, indicating that DeepCNAP satisfies the standards of both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). From the estimated BP, hypertension, prehypertension, hypotension, and the normal state were classified with 99.44, 97.58, 92.23, and 94.64% accuracy, respectively. DeepCNAP enables feasible real-time estimation of invasively measured ABP from noninvasive PPG. With its noninvasive nature, high accuracy, and clinical relevance, the proposed model could be valuable in general wards at hospitals and for wearable devices in daily life.

Background Continuous assessment of cerebral compensatory reserve is possible using the moving correlation between pulse amplitude of intra-cranial pressure (AMP) and intra-cranial pressure (ICP), called RAP. Little is known about the behavior and associations of this index in adult traumatic brain injury (TBI). The goal of this study is to evaluate the association between admission cerebral imaging findings and RAP over the course of the acute intensive care unit stay. Methods We retrospectively reviewed 358 adult TBI patients admitted to the Addenbrooke’s Hospital, University of Cambridge, from March 2005 to December 2016. Only non-craniectomy patients were studied. Using archived high frequency physiologic signals, RAP was derived and analyzed over the first 48 h and first 10 days of recording in each patient, using grand mean, percentage of time above various thresholds, and integrated area under the curve (AUC) of RAP over time. Associations between these values and admission computed tomography (CT) injury characteristics were evaluated. Results The integrated AUC, based on various thresholds of RAP, was statistically associated with admission CT markers of diffuse TBI and cerebral edema. Admission CT findings of cortical gyral effacement, lateral ventricle compression, diffuse cortical subarachnoid hemorrhage (SAH), thickness of cortical SAH, presence of bilateral contusions, and subcortical diffuse axonal injury (DAI) were all associated with AUC of RAP over time. Joncheere-Terpstra testing indicated a statistically significant increase in mean RAP AUC across ordinal categories of the abovementioned associated CT findings. Conclusions RAP is associated with cerebral CT injury patterns of diffuse injury and edema, providing some confirmation of its potential measurement of cerebral compensatory reserve in TBI.