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Distinguishing intentional from nonintentional actions through eeg and kinematic markers.

ABSTRACT: How can an intentional movement be distinguished from the same movement done nonintentionally? How can this distinction be drawn without asking the subject, or in patients who are unable to communicate? Here we address these questions, by focusing on blinking. This is one of the most frequent spontaneous actions in our daily life, but it can also be done intentionally. Furthermore, blinking is often spared in patients with severe brain injuries, and for some, it is the only way to report complex meanings. Using kinematic and EEG-based measures, we found that intentional and spontaneous blinking are preceded by different brain activities, even when they are indistinguishable. Unlike spontaneous ones, intentional blinks are characterized by a slow negative EEG drift, resembling the classic readiness potential. We investigated the theoretical implication of this finding in stochastic decision models as well as the practical significance of using brain-based signals to improve the discrimination between intentional and nonintentional actions. As proof of principle, we considered three brain-injured patients with rare neurological syndromes characterized by motor and communicative impairments. Although further research is needed, our results indicate that brain-based signals can offer a feasible way to infer intentionality even in absence of overt communication.

SUBMITTER: Derchi CC

PROVIDER: S-EPMC10213007 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Distinguishing intentional from nonintentional actions through eeg and kinematic markers.

Derchi C C CC Mikulan E E Mazza A A Casarotto S S Comanducci A A Fecchio M M Navarro J J Devalle G G Massimini M M Sinigaglia C C

Scientific reports 20230525 1

How can an intentional movement be distinguished from the same movement done nonintentionally? How can this distinction be drawn without asking the subject, or in patients who are unable to communicate? Here we address these questions, by focusing on blinking. This is one of the most frequent spontaneous actions in our daily life, but it can also be done intentionally. Furthermore, blinking is often spared in patients with severe brain injuries, and for some, it is the only way to report complex ...[more]

PMID: 37231006

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Project description:BackgroundKnowledge about non-surgical weight loss (WL) is scarce among people with severe obesity (SO). Lifestyle changes are primarily self-driven, occasionally accompanied by professional guidance and weight-management support. Weight regain and intervention discontinuation are common challenges among guidance and support programmes. In the current study, we describe a model of meaningful strategies for maintaining WL after SO based on the experiences of successful cases.MethodsAiming to investigate the experiences of WL and weight loss maintenance (WLM) (≥5 years) following SO, we designed a qualitative study. Ten adults of Norwegian ethnicity, eight women and two men aged from 27 to 59, participated in individual in-depth interviews. We recruited participants living in rural districts and cities across all four regions of Norway. The interviews concentrated on participants' experiences of losing weight and maintaining a lower weight over the long term. The transcripts were analysed with a rigorous method for thematic cross-case analysis, namely, systematic text condensation (STC).ResultsParticipants identified four experiential themes at the core of long-term WLM: (a) Owning the decision, (b) Creating self-reinforcement, (c) Sustaining a lifestyle-forming identity, and (d) Selecting support appropriate to one's own situation. These core themes represent the intentional level, functioning both as the foundation of and the momentum for sustaining WL. On the behavioural level, participants continued to take action for change, obtain results, record and reflect on their efforts and milestones, observe what worked and felt good, and receive recognition from others, thereby realising changes.ConclusionBased on these results, we propose a model of WLM after SO, suggesting that practices toward WLM on the behavioural level achieve meaning and sustainability through their relationship with a core intentional level found across participants' experiences. One implication is that the relationship between the intentional and behavioural levels might be more meaningful when discussing long-term WLM than the behaviours themselves.

Project description:Despite its proven research applications, it remains unknown whether surface electromyography (sEMG) can be used clinically to discriminate non-lame from lame conditions in horses. This study compared the classification performance of sEMG absolute value (sEMGabs) and asymmetry (sEMGasym) parameters, alongside validated kinematic upper-body asymmetry parameters, for distinguishing non-lame from induced fore- (iFL) and hindlimb (iHL) lameness. Bilateral sEMG and 3D-kinematic data were collected from clinically non-lame horses (n = 8) during in-hand trot. iFL and iHL (2-3/5 AAEP) were induced on separate days using a modified horseshoe, with baseline data initially collected each day. sEMG signals were DC-offset removed, high-pass filtered (40 Hz), and full-wave rectified. Normalized, average rectified value (ARV) was calculated for each muscle and stride (sEMGabs), with the difference between right and left-side ARV representing sEMGasym. Asymmetry parameters (MinDiff, MaxDiff, Hip Hike) were calculated from poll, withers, and pelvis vertical displacement. Receiver-operating-characteristic (ROC) and area under the curve (AUC) analysis determined the accuracy of each parameter for distinguishing baseline from iFL or iHL. Both sEMG parameters performed better for detecting iHL (0.97 ≥ AUC ≥ 0.48) compared to iFL (0.77 ≥ AUC ≥ 0.49). sEMGabs performed better (0.97 ≥ AUC ≥ 0.49) than sEMGasym (0.76 ≥ AUC ≥ 0.48) for detecting both iFL and iHL. Like previous studies, MinDiff Poll and Pelvis asymmetry parameters (MinDiff, MaxDiff, Hip Hike) demonstrated excellent discrimination for iFL and iHL, respectively (AUC > 0.95). Findings support future development of multivariate lameness-detection approaches that combine kinematics and sEMG. This may provide a more comprehensive approach to diagnosis, treatment, and monitoring of equine lameness, by measuring the underlying functional cause(s) at a neuromuscular level.

Dataset Information

Distinguishing intentional from nonintentional actions through eeg and kinematic markers.

Publications

Distinguishing intentional from nonintentional actions through eeg and kinematic markers.

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