An empirical comparison of different implicit measures to predict consumer choice

While past research has found that implicit measures are good predictors of affectively driven, but not cognitively driven, behavior it has not yet been tested which implicit measures best predict behavior. By implementing a consumer context, in the present experiment, we assessed two explicit measures (i.e. self-reported habit and tastiness) and three implicit measures (i.e. manikin task, affective priming, ID-EAST) in order to test the predictive validity of affectively versus cognitively driven choices. The results indicate that irrespective of whether participants chose affectively or cognitively, both explicit measures, but not the implicit measures, predicted consumer choice very strongly. Likewise, when comparing the predictive validity among all measures, the explicit measures were the best predictors of consumer choice. Theoretical implications and limitations of the study are discussed

Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features

Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems. We propose enhancements to different feature extractors, along with a support vector machine (SVM) classifier, to simultaneously improve classification accuracy and execution time during training and testing. We focus on the well-known common spatial pattern (CSP) and Riemannian covariance methods, and significantly extend these two feature extractors to multiscale temporal and spectral cases. The multiscale CSP features achieve 73.70$\pm$15.90% (mean$\pm$ standard deviation across 9 subjects) classification accuracy that surpasses the state-of-the-art method [1], 70.6$\pm$14.70%, on the 4-class BCI competition IV-2a dataset. The Riemannian covariance features outperform the CSP by achieving 74.27$\pm$15.5% accuracy and executing 9x faster in training and 4x faster in testing. Using more temporal windows for Riemannian features results in 75.47$\pm$12.8% accuracy with 1.6x faster testing than CSP.Comment: Published as a conference paper at the IEEE European Signal Processing Conference (EUSIPCO), 201

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, 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 5.48%. Our novel method further scales down the standard EEGNet at a negligible accuracy loss of 0.31% with 7.6x memory footprint reduction and a small accuracy loss of 2.51% with 15x reduction. The scaled models are deployed on a commercial Cortex-M4F MCU taking 101ms and consuming 4.28mJ per inference for operating the smallest model, and on a Cortex-M7 with 44ms and 18.1mJ per inference for the medium-sized model, enabling a fully autonomous, wearable, and accurate low-power BCI

Binarization Methods for Motor-Imagery Brain–Computer Interface Classification

Successful motor-imagery brain-computer interface (MI-BCI) algorithms either extract a large number of handcrafted features and train a classifier, or combine feature extraction and classification within deep convolutional neural networks (CNNs). Both approaches typically result in a set of real-valued weights, that pose challenges when targeting real-time execution on tightly resource-constrained devices. We propose methods for each of these approaches that allow transforming real-valued weights to binary numbers for efficient inference. Our first method, based on sparse bipolar random projection, projects a large number of real-valued Riemannian covariance features to a binary space, where a linear SVM classifier can be learned with binary weights too. By tuning the dimension of the binary embedding, we achieve almost the same accuracy in 4-class MI (<= 1.27% lower) compared to models with float16 weights, yet delivering a more compact model with simpler operations to execute. Second, we propose to use memory-augmented neural networks (MANNs) for MI-BCI such that the augmented memory is binarized. Our method replaces the fully connected layer of CNNs with a binary augmented memory using bipolar random projection, or learned projection. Our experimental results on EEGNet, an already compact CNN for MI-BCI, show that it can be compressed by 1.28x at iso-accuracy using the random projection. On the other hand, using the learned projection provides 3.89% higher accuracy but increases the memory size by 28.10x

Die Entwicklung der Berufswahlbereitschaft im jugendlichen Alter : das Beispiel von Schüler:innen mit sonderpädagogischem Förderbedarf am Übergang I

Die vorliegende Arbeit befasst sich mit dem Konstrukt der Berufswahlbereitschaft im Jugendalter. Es werden in Kurzform einige Forschungsergebnisse der vergangenen Jahrzehnte aufgeführt und ein Ausschnitt aus den aktuellen Forschungsbemühungen gegeben. Dabei werden das Berufswahlkompetenzmodell von Driesel-Lange et al. (2010) und entsprechende empirische Ergebnisse von Marciniak, Steiner und Hirschi (2019) näher vorgestellt. Bei beiden wird die Berufswahlbereitschaft in drei Hauptfaktoren unterteilt, Wissen, Motivation und Handlung. Im praktischen Teil der Arbeit werden die im Theorieteil präsentierten Einzelaspekte und -faktoren der Berufswahlbereitschaft an der Fokusgruppe lernbeeinträchtigte Jugendliche in der Praxis untersucht. Zudem werden spezifische Fördermöglichkeiten in Bezug auf die Berufswahlbereitschaft bei lernbehinderten Jugendlichen erfragt. Es wurden dazu vier Interviews mit Fachpersonen durchgeführt, welche mit lernbehinderten Jugendlichen in irgendeiner Form mit Bezug zur Berufswahlbereitschaft arbeiten. Die Interviewtexte wurden einer qualitativen Inhaltsanalyse nach Kuckartz unterzogen. Es zeigte sich, dass alle drei Hauptfaktoren des theoretischen Modells in der Praxis präsent sind, entweder durch ihnen zuordenbare Begrifflichkeiten, oder dann in gleichem Wortlaut und analoger Verwendung. Zudem wurden viele der Einzelfacetten genannt. Bei Lernbehinderten vorherrschende besondere Unterschiede in den Aspekten der Berufswahlbereitschaft konnten herauskristallisiert werden, so etwa im Selbstwissen bzw. im Wissen, welcher Beruf zu einem passt. Es wurden schliesslich auch mehrere Förderansätze in Bezug auf die Entwicklung der Berufswahlbereitschaft bei lernbehinderten Jugendlichen diskutiert, so beispielsweise spezifische Coachingunterstützung oder bedarfsangepasste Zwischenjahre, welche von den Expert:innen als sinnvoll erachtet werden. Zum Schluss hin werden die Grenzen der Arbeit aufgezeigt und mögliche weitere Schritte erörtert, welche an die bestehende Arbeit anknüpfen könnten, bspw. die Ausdehnung der Fokusgruppen bezüglich Berufswahlbereitschaft auch auf andere Beeinträchtigungen

Eine geistlich-weltliche Körperschaft im Alten Reich: quantitative Annäherungen an die deutschen Domkapitel

The present article is a summary of a quantitative Social History in three volumes (can be obtained directly from the author) treating of the 24 cathedral chapters of the Old German Empire in the 17th and 18th Century. The research on the cathedral chapters which were as well spiritual as secular corporations, until now is rather traditional in its methods, i.e. focusing on the constitutions and the biographies of the canons. Actually there are only a few monographies. Our study which comprehends all chapters examines 5 725 cases. The case-unit is not the person, but the prebend. The following variables are taken up: name (locality) of the chapter, dignitaries, degrees (for commoners), the ways of applying to and retiring from the chapters, social status (seven categories for the nobility, two for the commoners), advancements in rank, origin, cumulations. The completeness of the data is generally more than 90%, often towards 100%. All data is published in form of chronological lists of the canons in every chapter, besides an index of names is given. Therefore our work serves as a reference-book too. The data were processed with SPSS, crosstabulations and other statistics are published also. For regional inquiries the chapters were classified into three groups: Northern Germany, the chapters of the Knights of the Empire (i. e. Rhineland and Franconia), Southern Germany and Austria. To show the chronological development we divided the entire period (1601-1803) into four periods of about fifty years. The article presents some important results for every variable. Some general Statements are possible. From the viewpoint of social history the hypothesis of three regional types has been verified. Chapters at the border of the Empire form a particular group which shows more and more deviations to the Standard. On the other hand the chapters in the center assimilate. Spatial mobility decreases, local recruitment increases. There are tendencies to closeness and occasional provincialism. Cumulations increase in the second half of 18th Century. Canons from the middle and lower classes were almost completely eliminated during the two centuries. Thus the European feudal reaction can clearly be demonstrated using the example of the German cathedral chapters. Our research shows that the chapters get into a crisis in the late 18th Century. They could no more accomplish their functions as providing institutions for the German nobility. For many reasons the run to the prebends grew as well as the commoners' criticism influenced by the Enlightenment. They disapproved the loss of the chapters' spiritual functions, the prevalence of the nobility, the grewing exclusion of the commoners and the enormous cumulations. Proposals to a reform failed. The difficult Situation in the Empire during the Napoleonic Wars forced the secularization (1803) which brought the end to the old German cathedral chapters

MI-BMInet: An Efficient Convolutional Neural Network for Motor Imagery Brain--Machine Interfaces with EEG Channel Selection

A brain--machine interface (BMI) based on motor imagery (MI) enables the control of devices using brain signals while the subject imagines performing a movement. It plays an important role in prosthesis control and motor rehabilitation and is a crucial element towards the future Internet of Minds (IoM). To improve user comfort, preserve data privacy, and reduce the system's latency, a new trend in wearable BMIs is to embed algorithms on low-power microcontroller units (MCUs) to process the electroencephalographic (EEG) data in real-time close to the sensors into the wearable device. However, most of the classification models present in the literature are too resource-demanding, making them unfit for low-power MCUs. This paper proposes an efficient convolutional neural network (CNN) for EEG-based MI classification that achieves comparable accuracy while being orders of magnitude less resource-demanding and significantly more energy-efficient than state-of-the-art (SoA) models for a long-lifetime battery operation. We propose an automatic channel selection method based on spatial filters and quantize both weights and activations to 8-bit precision to further reduce the model complexity with negligible accuracy loss. Finally, we efficiently implement and evaluate the proposed models on a parallel ultra-low power (PULP) MCU. The most energy-efficient solution consumes only 50.10 uJ with an inference runtime of 5.53 ms and an accuracy of 82.51% while using 6.4x fewer EEG channels, becoming the new SoA for embedded MI-BMI and defining a new Pareto frontier in the three-way trade-off among accuracy, resource cost, and power usage

Occupational therapy-based energy management education in people with post-COVID-19 condition-related fatigue : results from a focus group discussion

Persons with post-COVID-19 conditions have prolonged symptoms and longer-term consequences which can prevent them from returning to previous everyday functioning. Fatigue is the most frequent symptom reported in literature. Occupational therapists (OTs) are specialized in client-centered problem analysis, counseling, and education to recover occupational engagement and performance in everyday life. Since the beginning of the COVID-19 pandemic, OTs have been challenged to respond with services adequate to the needs of this patient group. Energy management education (EME) was initially developed for persons with multiple sclerosis-related fatigue and then made independent of diagnosis suitable to persons living with chronic disease-related fatigue. EME, a structured self-management education, is becoming a part of the new services. This study was aimed at exploring the initial experiences of OTs using the EME protocol and materials with persons with postacute COVID-19 and/or post-COVID-19 condition-related fatigue and gathering their recommendations for improvements and adaptions. One online focus group discussion took place in May 2021 with OTs experienced in using the EME protocol. The topics addressed were the institutional context of the OTs and their experiences during the treatment. A thematic analysis was performed. According to nine OTs working in different settings in Switzerland, the EME protocol is exploitable in both in- and outpatient settings and was judged appropriate by them, even if the EME materials can be improved. The main challenges for the OTs were the short period their patients had lived with fatigue; the discrepancy between self-concept, self-perception, and performance; and the insecurity, fear, and anxiety related to recovery. Further research is needed to include the perspective of EME participants and to measure quantitative outcomes such as fatigue impact, self-efficacy, occupational performance, and quality of life. Until the existing EME protocol is improved, it is applicable to persons with post-COVID-19 condition-related fatigue

Mixed-Precision Quantization and Parallel Implementation of Multispectral Riemannian Classification for Brain--Machine Interfaces

With Motor-Imagery (MI) Brain--Machine Interfaces (BMIs) we may control machines by merely thinking of performing a motor action. Practical use cases require a wearable solution where the classification of the brain signals is done locally near the sensor using machine learning models embedded on energy-efficient microcontroller units (MCUs), for assured privacy, user comfort, and long-term usage. In this work, we provide practical insights on the accuracy-cost tradeoff for embedded BMI solutions. Our proposed Multispectral Riemannian Classifier reaches 75.1% accuracy on 4-class MI task. We further scale down the model by quantizing it to mixed-precision representations with a minimal accuracy loss of 1%, which is still 3.2% more accurate than the state-of-the-art embedded convolutional neural network. We implement the model on a low-power MCU with parallel processing units taking only 33.39ms and consuming 1.304mJ per classification