Compressing Subject-specific Brain-Computer Interface Models into One Model by Superposition in Hyperdimensional Space

Accurate multiclass classification of electroencephalography (EEG) signals is still a challenging task towards the development of reliable motor imagery brain-computer interfaces (MI-BCIs). Deep learning algorithms have been recently used in this area to deliver a compact and accurate model. Reaching high-level of accuracy requires to store subjects-specific trained models that cannot be achieved with an otherwise compact model trained globally across all subjects. In this paper, we propose a new methodology that closes the gap between these two extreme modeling approaches: we reduce the overall storage requirements by superimposing many subject-specific models into one single model such that it can be reliably decomposed, after retraining, to its constituent models while providing a trade-off between compression ratio and accuracy. Our method makes the use of unexploited capacity of trained models by orthogonalizing parameters in a hyperdimensional space, followed by iterative retraining to compensate noisy decomposition. This method can be applied to various layers of deep inference models. Experimental results on the 4-class BCI competition IV-2a dataset show that our method exploits unutilized capacity for compression and surpasses the accuracy of two state-of-the-art networks: (1) it compresses the smallest network, EEGNet [1], by 1.9 7, and increases its accuracy by 2.41% (74.73% vs. 72.32%); (2) using a relatively larger Shallow ConvNet [2], our method achieves 2.95 x compression as well as 1.4% higher accuracy (75.05% vs. 73.59%)

An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing

This paper presents an accurate and robust embedded motor-imagery brain-computer interface (MI-BCI). The proposed novel model, based on EEGNet [1], matches the requirements of memory footprint and computational resources of low-power microcontroller units (MCUs), such as the ARM Cortex-M family. Furthermore, the paper presents a set of methods, including temporal downsampling, channel selection, and narrowing of the classification window, to further scale down the model to relax memory requirements with negligible accuracy degradation. Experimental results on the Physionet EEG Motor Movement/Imagery Dataset show that standard EEGNet achieves 82.43%, 75.07%, and 65.07% classification accuracy on 2-, 3-, and 4-class MI tasks in global validation, outperforming the state-of-the-art (SoA) convolutional neural network (CNN) by 2.05%, 5.25%, and 6.49%. Our novel method further scales down the standard EEGNet at a negligible accuracy loss of 0.31% with 7.6 7 memory footprint reduction and a small accuracy loss of 2.51% with 15 7 reduction. The scaled models are deployed on a commercial Cortex-M4F MCU taking 101 ms and consuming 4.28 mJ per inference for operating the smallest model, and on a Cortex-M7 with 44 ms and 18.1 mJ per inference for the medium-sized model, enabling a fully autonomous, wearable, and accurate low-power BCI

Deltoid muscle shape analysis with magnetic resonance imaging in patients with chronic rotator cuff tears

BACKGROUND: It seems appropriate to assume, that for a full and strong global shoulder function a normally innervated and active deltoid muscle is indispensable. We set out to analyse the size and shape of the deltoid muscle on MR-arthrographies, and analyse its influence on shoulder function and its adaption (i.e. atrophy) for reduced shoulder function. METHODS: The fatty infiltration (Goutallier stages), atrophy (tangent sign) and selective myotendinous retraction of the rotator cuff, as well as the thickness and the area of seven anatomically defined segments of the deltoid muscle were measured on MR-arthrographies and correlated with shoulder function (i.e. active abduction). Included were 116 patients, suffering of a rotator cuff tear with shoulder mobility ranging from pseudoparalysis to free mobility. Kolmogorov-Smirnov test was used to determine the distribution of the data before either Spearman or Pearson correlation and a multiple regression was applied to reveal the correlations. RESULTS: Our developed method for measuring deltoid area and thickness showed to be reproducible with excellent interobserver correlations (r = 0.814-0.982).The analysis of influencing factors on active abduction revealed a weak influence of the amount of SSP tendon (r = -0.25; p < 0.01) and muscle retraction (r = -0.27; p < 0.01) as well as the stage of fatty muscle infiltration (GFDI: r = -0.36; p < 0.01). Unexpectedly however, we were unable to detect a relation of the deltoid muscle shape with the degree of active glenohumeral abduction. Furthermore, long-standing rotator cuff tears did not appear to influence the deltoid shape, i.e. did not lead to muscle atrophy. CONCLUSIONS: Our data support that in chronic rotator cuff tears, there seems to be no disadvantage to exhausting conservative treatment and to delay implantation of reverse total shoulder arthroplasty, as the shape of deltoid muscle seems only to be influenced by natural aging, but to be independent of reduced shoulder motion

Einfluss der Sakrumfraktur auf das funktionelle Langzeitergebnis von Beckenringverletzungen

In der Akutphase umfasst die Behandlung der Beckenringverletzung mit Beteiligung des iliosakralen Komplexes die effiziente Blutungskontrolle und Stabilisierung des Beckenrings. Für das Langzeitresultat sind jedoch neurologische Ausfälle, Fehlverheilungen des hinteren Beckenrings mit tieflumbalen Schmerzen und urologische Komplikationen entscheidend. Zwischen 1991 und 2000 wurden in unserer Klinik 173Patienten mit Sakrumfrakturen behandelt. Diese wurden im Rahmen einer lateralen Kompressionsfraktur (AO-Klassifikation TypB2) oder einer "vertical-shear-" (Typ-C-)Verletzung mit einer Dislokation von 1cm wurden operativ (n=33, 19%) versorgt. 112Patienten wurden nach durchschnittlich 4,9Jahren nachkontrolliert. Von den 39Patienten mit neurologischen Ausfällen (35%) zeigten lediglich 4 eine vollständige neurologische Erholung. Chronische tieflumbale Schmerzen traten selten (n=8, 7%) und nur bei einer Typ-C-Verletzung auf. Die geringe Inzidenz an lumbalen Schmerzen rechtfertigt die konservative Therapie wenig dislozierter (<1cm) Sakrumfrakturen. Entscheidend für das Langzeitergebnis sind neurologische Defizite, die bei 30% aller Patienten persistiere