0000000001043947
AUTHOR
Jukka-pekka Kauppi
showing 15 related works from this author
Time-resolved classification of dog brain signals reveals early processing of faces, species and emotion
2020
Dogs process faces and emotional expressions much like humans, but the time windows important for face processing in dogs are largely unknown. By combining our non-invasive electroencephalography (EEG) protocol on dogs with machine-learning algorithms, we show category-specific dog brain responses to pictures of human and dog facial expressions, objects, and phase-scrambled faces. We trained a support vector machine classifier with spatiotemporal EEG data to discriminate between responses to pairs of images. The classification accuracy was highest for humans or dogs vs. scrambled images, with most informative time intervals of 100–140 ms and 240–280 ms. We also detected a response sensitive…
Collaborative roles of Temporoparietal Junction and Dorsolateral Prefrontal Cortex in Different Types of Behavioural Flexibility
2017
AbstractBehavioural flexibility is essential for everyday life. This involves shifting attention between different perspectives. Previous studies suggest that flexibility is mainly subserved by the dorsolateral prefrontal cortex (DLPFC). However, although rarely emphasized, the temporoparietal junction (TPJ) is frequently recruited during flexible behaviour. A crucial question is whether TPJ plays a role in different types of flexibility, compared to its limited role in perceptual flexibility. We hypothesized that TPJ activity during diverse flexibility tasks plays a common role in stimulus-driven attention-shifting, thereby contributing to different types of flexibility, and thus the colla…
Association Between Implementation of the Athlete Biological Passport and Female Elite Runners’ Performance
2020
The purpose of this research was to evaluate the performances of female middle- and long-distance runners before and after the implementation of a new antidoping strategy (the Athlete Biological Passport [ABP]) in a country accused of systematic doping. A retrospective analysis of the results of Russian National Championships from 2008 to 2017 was performed. The 8 best female performances for the 800-m, 1500-m, 3000-m steeplechase, 5000-m, and 10,000-m events from the semifinals and finals were analyzed. The yearly number of athletes fulfilling standard qualifications for international competitions was also evaluated. Overall, numbers of athletes banned for doping in 2008–2017 were calculat…
Brain and behavioral alterations in subjects with social anxiety dominated by empathic embarrassment
2020
Significance People are increasingly affected by social anxiety that includes emotional hypersensitivity and inaccurate interpretation of social encounters, and varies markedly in its subjective manifestations. We searched for insights into the underlying neurocognitive mechanisms of Taijin-kyofusho (TKS), a specific subtype of social-anxiety disorder common in East Asia and dominated by empathic or other-oriented embarrassment. We found TKS to be characterized by enhanced affective and reduced cognitive empathy. Moreover, analysis of functional MRI data—collected while subjects viewed videos of badly singing people—revealed disruption of the cognitive–empathy network, possibly obstructing …
Decoding Emotional Valence from Electroencephalographic Rhythmic Activity
2017
We attempt to decode emotional valence from electroencephalographic rhythmic activity in a naturalistic setting. We employ a data-driven method developed in a previous study, Spectral Linear Discriminant Analysis, to discover the relationships between the classification task and independent neuronal sources, optimally utilizing multiple frequency bands. A detailed investigation of the classifier provides insight into the neuronal sources related with emotional valence, and the individual differences of the subjects in processing emotions. Our findings show: (1) sources whose locations are similar across subjects are consistently involved in emotional responses, with the involvement of parie…
Functional Brain Segmentation Using Inter-Subject Correlation in fMRI
2016
The human brain continuously processes massive amounts of rich sensory information. To better understand such highly complex brain processes, modern neuroimaging studies are increasingly utilizing experimental setups that better mimic daily‐life situations. A new exploratory data‐analysis approach, functional segmentation inter‐subject correlation analysis (FuSeISC), was proposed to facilitate the analysis of functional magnetic resonance (fMRI) data sets collected in these experiments. The method provides a new type of functional segmentation of brain areas, not only characterizing areas that display similar processing across subjects but also areas in which processing across subjects is h…
Talent identification in soccer using a one-class support vector machine
2019
Abstract Identifying potential future elite athletes is important in many sporting events. The successful identification of potential future elite athletes at an early age would help to provide high-quality coaching and training environments in which to optimize their development. However, a large variety of different skills and qualities are needed to succeed in elite sports, making talent identification generally a complex and multifaceted problem. Due to the rarity of elite athletes, datasets are inherently imbalanced, making classical statistical inference difficult. Therefore, we approach talent identification as an anomaly detection problem. We trained a nonlinear one-class support ve…
Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes
2022
Background: Injury risk prediction is an emerging field in which more research is needed to recognize the best practices for accurate injury risk assessment. Important issues related to predictive machine learning need to be considered, for example, to avoid overinterpreting the observed prediction performance. Purpose: To carefully investigate the predictive potential of multiple predictive machine learning methods on a large set of risk factor data for anterior cruciate ligament (ACL) injury; the proposed approach takes into account the effect of chance and random variations in prediction performance. Study Design: Case-control study; Level of evidence, 3. Methods: The authors used 3-dime…
Neural-level associations of non-verbal pragmatic comprehension in young Finnish autistic adults
2021
This video-based study examines the pragmatic non-verbal comprehension skills and corresponding neural-level findings in young Finnish autistic adults, and controls. Items from the Assessment Battery of Communication (ABaCo) were chosen to evaluate the comprehension of non-verbal communication. Inter-subject correlation (ISC) analysis of the functional magnetic resonance imaging data was used to reveal the synchrony of brain activation across participants during the viewing of pragmatically complex scenes of ABaCo videos. The results showed a significant difference between the ISC maps of the autistic and control groups in tasks involving the comprehension of non-verbal communication, there…
A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns
2020
Previous studies have suggested that runners can be subgrouped based on homogeneous gait patterns, however, no previous study has assessed the presence of such subgroups in a population of individuals across a wide variety of injuries. Therefore, the purpose of this study was to assess whether distinct subgroups with homogeneous running patterns can be identified among a large group of injured and healthy runners and whether identified subgroups are associated with specific injury location. Three‐dimensional kinematic data from 291 injured and healthy runners, representing both sexes and a wide range of ages (10‐66 years) was clustered using hierarchical cluster analysis. Cluster analysis r…
Comparing fMRI inter-subject correlations between groups using permutation tests
2018
AbstractInter-subject correlation (ISC) based analysis is a conceptually simple approach to analyze functional magnetic resonance imaging (fMRI) data acquired under naturalistic stimuli such as a movie. We describe and validate the statistical approaches for comparing ISCs between two groups of subjects implemented in the ISC toolbox, which is an open source software package for ISC-based analysis of fMRI data. The approaches are based on permutation tests. We validated the approaches using five different data sets from the ICBM functional reference battery tasks. First, we created five null datasets (one for each task) by dividing the subjects into two matched groups and assumed that no gr…
Prediction of active peak force using a multilayer perceptron
2017
Both kinematic parameters and ground reaction forces (GRFs) are necessary for understanding the biomechanics of running. Kinematic information of a runner is typically measured by a motion capture system whereas GRF during the support phase of running is measured by force platforms. To analyze both kinematics and kinetics of a runner over several subsequent contacts, an instrumented treadmill or alternatively several force platforms installed over a regulated space are available options, but they are highly immovable, expensive, and sometimes even impractical options. Naturally, it would be highly useful to predict GRFs using a motion capture system only and this way reduce costs and comple…
sj-pdf-1-ajs-10.1177_03635465221112095 – Supplemental material for Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening T…
2022
Supplemental material, sj-pdf-1-ajs-10.1177_03635465221112095 for Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes by Susanne Jauhiainen, Jukka-Pekka Kauppi, Tron Krosshaug, Roald Bahr, Julia Bartsch and Sami Äyrämö in The American Journal of Sports Medicine
Precision exercise medicine: predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine lear…
2021
Objectives: To assess the ability to predict individual unfavourable future status and development in the 20m shuttle run test (20MSRT) during adolescence with machine learning (random forest (RF) classifier). Methods: Data from a 2-year observational study (2013‒2015, 12.4±1.3 years, n=633, 50% girls), with 48 baseline characteristics (questionnaires (demographics, physical, psychological, social and lifestyle factors), objective measurements (anthropometrics, fitness characteristics, physical activity, body composition and academic scores)) were used to predict: (Task 1) unfavourable future 20MSRT status (identification of individuals in the lowest 20MSRT tertile after 2 years), and (Task…
Information Extraction from Binary Skill Assessment Data with Machine Learning
2021
Strength training exercises are essential for rehabilitation, improving our health as well as in sports. For optimal and safe training, educators and trainers in the industry should comprehend exercise form or technique. Currently, there is a lack of tools measuring in-depth skills of strength training experts. In this study, we investigate how data mining methods can be used to identify novel and useful skill patterns from a binary multiple choice questionnaire test designed to measure the knowledge level of strength training experts. A skill test assessing exercise technique expertise and comprehension was answered by 507 fitness professionals with varying backgrounds. A triangulated appr…