Characterization of a Raspberry Pi as the Core for a Low-cost Multimodal EEG-fNIRS Platform.

Poor understanding of brain recovery after injury, sparsity of evaluations and limited availability of healthcare services hinders the success of neurorehabilitation programs in rural communities. The availability of neuroimaging ca-pacities in remote communities can alleviate this scenario supporting neurorehabilitation programs in remote settings. This research aims at building a multimodal EEG-fNIRS neuroimaging platform deployable to rural communities to support neurorehabilitation efforts. A Raspberry Pi 4 is chosen as the CPU for the platform responsible for presenting the neurorehabilitation stimuli, acquiring, processing and storing concurrent neuroimaging records as well as the proper synchronization between the neuroimaging streams. We present here two experiments to assess the feasibility and characterization of the Raspberry Pi as the core for a multimodal EEG-fNIRS neuroimaging platform; one over controlled conditions using a combination of synthetic and real data, and another from a full test during resting state. CPU usage, RAM usage and operation temperature were measured during the tests with mean operational records below 40% for CPU cores, 13.6% for memory and 58.85 ° C for temperatures. Package loss was inexistent on synthetic data and negligible on experimental data. Current consumption can be satisfied with a 1000 mAh 5V battery. The Raspberry Pi 4 was able to cope with the required workload in conditions of operation similar to those needed to support a neurorehabilitation evaluation.

Mass segmentation of mammograms using Markov models associated with constrained clustering.

In this paper, we propose four variants of the Markov random field model by using constrained clustering for breast mass segmentation. These variants were tested with a set of images extracted from a public database. The obtained results have shown that the proposed variants, which allow to include additional information in the form of constraints to the clustering process, present better visual segmentation results than the original model, as well as a lower final energy which implies a better quality in the final segmentation. Specifically, the centroid initialization method used by our variants allows us to locate about 90% of the regions of interest that contain a mass, which subsequently with the pairwise constraints helped us recover a maximum of 93% of the masses. The segmentation results are also quantitatively evaluated using three supervised segmentation measures. These measures show that the mass segmentation quality of the proposed variants, considering the breast density level, is consistent with the corresponding segmentation annotated by specialized radiologists.

Automation of motor dexterity assessment.

Motor dexterity assessment is regularly performed in rehabilitation wards to establish patient status and automatization for such routinary task is sought. A system for automatizing the assessment of motor dexterity based on the Fugl-Meyer scale and with loose restrictions on sensing technologies is presented. The system consists of two main elements: 1) A data representation that abstracts the low level information obtained from a variety of sensors, into a highly separable low dimensionality encoding employing t-distributed Stochastic Neighbourhood Embedding, and, 2) central to this communication, a multi-label classifier that boosts classification rates by exploiting the fact that the classes corresponding to the individual exercises are naturally organized as a network. Depending on the targeted therapeutic movement class labels i.e. exercises scores, are highly correlated-patients who perform well in one, tends to perform well in related exercises-; and critically no node can be used as proxy of others - an exercise does not encode the information of other exercises. Over data from a cohort of 20 patients, the novel classifier outperforms classical Naive Bayes, random forest and variants of support vector machines (ANOVA: p < 0.001). The novel multi-label classification strategy fulfills an automatic system for motor dexterity assessment, with implications for lessening therapist's workloads, reducing healthcare costs and providing support for home-based virtual rehabilitation and telerehabilitation alternatives.

Robot training for hand motor recovery in subacute stroke patients: A randomized controlled trial.

BACKGROUND: Evidence of superiority of robot training for the hand over classical therapies in stroke patients remains controversial. During the subacute stage, hand training is likely to be the most useful. AIM: To establish whether robot active assisted therapies provides any additional motor recovery for the hand when administered during the subacute stage (<4 months from event) in a Mexican adult population diagnosed with stroke. HYPOTHESIS: Compared to classical occupational therapy, robot based therapies for hand recovery will show significant differences at subacute stages. TRIAL DESIGN: A randomized clinical trial. METHODS: A between subjects randomized controlled trial was carried out on subacute stroke patients (n = 17) comparing robot active assisted therapy (RT) with a classical occupational therapy (OT). Both groups received 40 sessions ensuring at least 300 repetitions per session. Treatment duration was (mean ± std) 2.18 ± 1.25 months for the control group and 2.44 ± 0.88 months for the study group. The primary outcome was motor dexterity changes assessed with the Fugl-Meyer (FMA) and the Motricity Index (MI). RESULTS: Both groups (OT: n = 8; RT: n = 9) exhibited significant improvements over time (Non-parametric Cliff's delta-within effect sizes: dwOT-FMA = 0.5, dwOT-MI = 0.5, dwRT-FMA = 1, dwRT-MI = 1). Regarding differences between the therapies; the Fugl-Meyer score indicated a significant advantage for the hand training with the robot (FMA hand: WRS: W = 8, p <0.01), whilst the Motricity index suggested a greater improvement (size effect) in hand prehension for RT with respect to OT but failed to reach significance (MI prehension: W = 17.5, p = 0.080). No harm occurred. CONCLUSIONS: Robotic therapies may be useful during the subacute stages of stroke - both endpoints (FM hand and MI prehension) showed the expected trend with bigger effect size for the robotic intervention. Additional benefit of the robotic therapy over the control therapy was only significant when the difference was measured with FM, demanding further investigation with larger samples. Implications of this study are important for decision making during therapy administration and resource allocation.

Gesture therapy: an upper limb virtual reality-based motor rehabilitation platform.

Virtual reality platforms capable of assisting rehabilitation must provide support for rehabilitation principles: promote repetition, task oriented training, appropriate feedback, and a motivating environment. As such, development of these platforms is a complex process which has not yet reached maturity. This paper presents our efforts to contribute to this field, presenting Gesture Therapy, a virtual reality-based platform for rehabilitation of the upper limb. We describe the system architecture and main features of the platform and provide preliminary evidence of the feasibility of the platform in its current status.

Neural reorganization accompanying upper limb motor rehabilitation from stroke with virtual reality-based gesture therapy.

BACKGROUND: Gesture Therapy is an upper limb virtual reality rehabilitation-based therapy for stroke survivors. It promotes motor rehabilitation by challenging patients with simple computer games representative of daily activities for self-support. This therapy has demonstrated clinical value, but the underlying functional neural reorganization changes associated with this therapy that are responsible for the behavioral improvements are not yet known. OBJECTIVE: We sought to quantify the occurrence of neural reorganization strategies that underlie motor improvements as they occur during the practice of Gesture Therapy and to identify those strategies linked to a better prognosis. METHODS: Functional magnetic resonance imaging (fMRI) neuroscans were longitudinally collected at 4 time points during Gesture Therapy administration to 8 patients. Behavioral improvements were monitored using the Fugl-Meyer scale and Motricity Index. Activation loci were anatomically labelled and translated to reorganization strategies. Strategies are quantified by counting the number of active clusters in brain regions tied to them. RESULTS: All patients demonstrated significant behavioral improvements (P < .05). Contralesional activation of the unaffected motor cortex, cerebellar recruitment, and compensatory prefrontal cortex activation were the most prominent strategies evoked. A strong and significant correlation between motor dexterity upon commencing therapy and total recruited activity was found (r2 = 0.80; P < .05), and overall brain activity during therapy was inversely related to normalized behavioral improvements (r2 = 0.64; P < .05). CONCLUSIONS: Prefrontal cortex and cerebellar activity are the driving forces of the recovery associated with Gesture Therapy. The relation between behavioral and brain changes suggests that those with stronger impairment benefit the most from this paradigm.