Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system.

Introduction: Brain-computer interfaces (BCIs) based on functional electrical stimulation have been used for upper extremity motor rehabilitation after stroke. However, little is known about their efficacy for multiple BCI treatments. In this study, 19 stroke patients participated in 25 upper extremity followed by 25 lower extremity BCI training sessions. Methods: Patients' functional state was assessed using two sets of clinical scales for the two BCI treatments. The Upper Extremity Fugl-Meyer Assessment (FMA-UE) and the 10-Meter Walk Test (10MWT) were the primary outcome measures for the upper and lower extremity BCI treatments, respectively. Results: Patients' motor function as assessed by the FMA-UE improved by an average of 4.2 points (p < 0.001) following upper extremity BCI treatment. In addition, improvements in activities of daily living and clinically relevant improvements in hand and finger spasticity were observed. Patients showed further improvements after the lower extremity BCI treatment, with walking speed as measured by the 10MWT increasing by 0.15 m/s (p = 0.001), reflecting a substantial meaningful change. Furthermore, a clinically relevant improvement in ankle spasticity and balance and mobility were observed. Discussion: The results of the current study provide evidence that both upper and lower extremity BCI treatments, as well as their combination, are effective in facilitating functional improvements after stroke. In addition, and most importantly improvements did not stop after the first 25 upper extremity BCI sessions.

Real-time estimation of EEG-based engagement in different tasks.

Objective.Recent trends in brain-computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG).Approach.Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects' perceived engagement was quantified using a questionnaire.Main results.The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects' perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (p= 0.025 andp<0.001, respectively); greater during medium and fast compared to slow Tetris speed (p<0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (rrm= 0.44,p<0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states.Significance.This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications.

Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility.

The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists' NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists' estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required.

Characterization of High-Gamma Activity in Electrocorticographic Signals.

Introduction: Electrocorticographic (ECoG) high-gamma activity (HGA) is a widely recognized and robust neural correlate of cognition and behavior. However, fundamental signal properties of HGA, such as the high-gamma frequency band or temporal dynamics of HGA, have never been systematically characterized. As a result, HGA estimators are often poorly adjusted, such that they miss valuable physiological information. Methods: To address these issues, we conducted a thorough qualitative and quantitative characterization of HGA in ECoG signals. Our study is based on ECoG signals recorded from 18 epilepsy patients while performing motor control, listening, and visual perception tasks. In this study, we first categorize HGA into HGA types based on the cognitive/behavioral task. For each HGA type, we then systematically quantify three fundamental signal properties of HGA: the high-gamma frequency band, the HGA bandwidth, and the temporal dynamics of HGA. Results: The high-gamma frequency band strongly varies across subjects and across cognitive/behavioral tasks. In addition, HGA time courses have lowpass character, with transients limited to 10 Hz. The task-related rise time and duration of these HGA time courses depend on the individual subject and cognitive/behavioral task. Task-related HGA amplitudes are comparable across the investigated tasks. Discussion: This study is of high practical relevance because it provides a systematic basis for optimizing experiment design, ECoG acquisition and processing, and HGA estimation. Our results reveal previously unknown characteristics of HGA, the physiological principles of which need to be investigated in further studies.

Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing.

Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large). As the baseline for our workflow testing, we use a Unet++ model trained on histological images of canine lymphoma with individually labeled nuclei. As an alternative to the Unet++, we also used a publicly available pre-trained and unmodified instance segmentation model called Stardist to demonstrate that our modular classification workflow can be combined with different types of segmentation models if they can provide proper nuclei segmentation. Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. Our proposed approach is modular and comprehensible, thus allowing adaptation for specific tasks and increasing the users' trust in computer-assisted image classification.

Time-Variant Linear Discriminant Analysis Improves Hand Gesture and Finger Movement Decoding for Invasive Brain-Computer Interfaces.

Invasive brain-computer interfaces yield remarkable performance in a multitude of applications. For classification experiments, high-gamma bandpower features and linear discriminant analysis (LDA) are commonly used due to simplicity and robustness. However, LDA is inherently static and not suited to account for transient information that is typically present in high-gamma features. To resolve this issue, we here present an extension of LDA to the time-variant feature space. We call this method time-variant linear discriminant analysis (TVLDA). It intrinsically provides a feature reduction stage, which makes external approaches thereto obsolete, such as feature selection techniques or common spatial patterns (CSPs). As well, we propose a time-domain whitening stage which equalizes the pronounced 1/f-shape of the typical brain-wave spectrum. We evaluated our proposed architecture based on recordings from 15 epilepsy patients with temporarily implanted subdural grids, who participated in additional research experiments besides clinical treatment. The experiments featured two different motor tasks involving three high-level gestures and individual finger movement. We used log-transformed bandpower features from the high-gamma band (50-300 Hz, excluding power-line harmonics) for classification. On average, whitening improved the classification performance by about 11%. On whitened data, TVLDA outperformed LDA with feature selection by 11.8%, LDA with CSPs by 13.9%, and regularized LDA with vectorized features by 16.4%. At the same time, TVLDA only required one or two internal features to achieve this. TVLDA provides stable results even if very few trials are available. It is easy to implement, fully automatic and deterministic. Due to its low complexity, TVLDA is suited for real-time brain-computer interfaces. Training is done in less than a second. TVLDA performed particularly well in experiments with data from high-density electrode arrays. For example, the three high-level gestures were correctly identified at a rate of 99% over all subjects. Similarly, the decoding accuracy of individual fingers was 96% on average over all subjects. To our knowledge, these mean accuracies are the highest ever reported for three-class and five-class motor-control BCIs.

An electrocorticographic BCI using code-based VEP for control in video applications: a single-subject study.

A brain-computer-interface (BCI) allows the user to control a device or software with brain activity. Many BCIs rely on visual stimuli with constant stimulation cycles that elicit steady-state visual evoked potentials (SSVEP) in the electroencephalogram (EEG). This EEG response can be generated with a LED or a computer screen flashing at a constant frequency, and similar EEG activity can be elicited with pseudo-random stimulation sequences on a screen (code-based BCI). Using electrocorticography (ECoG) instead of EEG promises higher spatial and temporal resolution and leads to more dominant evoked potentials due to visual stimulation. This work is focused on BCIs based on visual evoked potentials (VEP) and its capability as a continuous control interface for augmentation of video applications. One 35 year old female subject with implanted subdural grids participated in the study. The task was to select one out of four visual targets, while each was flickering with a code sequence. After a calibration run including 200 code sequences, a linear classifier was used during an evaluation run to identify the selected visual target based on the generated code-based VEPs over 20 trials. Multiple ECoG buffer lengths were tested and the subject reached a mean online classification accuracy of 99.21% for a window length of 3.15 s. Finally, the subject performed an unsupervised free run in combination with visual feedback of the current selection. Additionally, an algorithm was implemented that allowed to suppress false positive selections and this allowed the subject to start and stop the BCI at any time. The code-based BCI system attained very high online accuracy, which makes this approach very promising for control applications where a continuous control signal is needed.

Accuracy of a P300 speller for people with motor impairments: a comparison.

A Brain-Computer Interface (BCI) provides a completely new output pathway that can provide an additional option for a person to express himself/herself if he/she suffers a disorder like amyotrophic lateral sclerosis (ALS), brainstem stroke, brain or spinal cord injury or other diseases which impair the function of the common output pathways which are responsible for the control of muscles. For a P300 based BCI a matrix of randomly flashing characters is presented to the participant. To spell a character the person has to attend to it and to count how many times the character flashes. Although most BCIs are designed to help people with disabilities, they are mainly tested on healthy, young subjects who may achieve better results than people with impairments. In this study we compare measurements, performed on people suffering motor impairments, such as stroke or ALS, to measurements performed on healthy people. The overall accuracy of the persons with motor impairments reached 70.1% in comparison to 91% obtained for the group of healthy subjects. When looking at single subjects, one interesting example shows that under certain circumstances, when it is difficult for a patient to concentrate on one character for a longer period of time, the accuracy is higher when fewer flashes (i.e., stimuli) are presented. Furthermore, the influence of several tuning parameters is discussed as it shows that for some participants adaptations for achieving valuable spelling results are required. Finally, exclusion criteria for people who are not able to use the device are defined.