Results 31 to 40 of about 8,339 (216)
Lessons Learned From a Delayed‐Start Trial of Modafinil for Freezing of Gait in Parkinson's Disease
ABSTRACT Objective Freezing of gait (FOG) in people with Parkinson's disease (PwPD) is debilitating and has limited treatments. Modafinil modulates beta/gamma band activity in the pedunculopontine nucleus (PPN), like PPN deep brain stimulation. We therefore tested the hypothesis that Modafinil would improve FOG in PwPD.
Tuhin Virmani +8 more
wiley +1 more source
System design for using multimodal trace data in modeling self-regulated learning
Self-regulated learning (SRL) integrates monitoring and controlling of cognitive, affective, metacognitive, and motivational processes during learning in pursuit of goals. Researchers have begun using multimodal data (e.g., concurrent verbalizations, eye
Elizabeth Brooke Cloude +4 more
doaj +1 more source
Intrinsically motivated multimodal structure learning [PDF]
We present a long-term intrinsically motivated structure learning method for modeling transition dynamics during controlled interactions between a robot and semi-permanent structures in the world. In particular, we discuss how partially-observable state is represented using distributions over a Markovian state and build models of objects that predict ...
Wong, Jay Ming, Grupen, Roderic A.
openaire +2 more sources
ABSTRACT Objective Accurate localization of epileptogenic tubers (ETs) in patients with tuberous sclerosis complex (TSC) is essential but challenging, as these tubers lack distinct pathological or genetic markers to differentiate them from other cortical tubers.
Tinghong Liu +11 more
wiley +1 more source
This study examines the efficacy of a multimodal online bilingual dictionary based on cognitive linguistics in order to explore the advantages and limitations of explicit multimodal L2 vocabulary learning.
Takeshi Sato
doaj +1 more source
Progressive Learning of a Multimodal Classifier Accounting for Different Modality Combinations
In classification tasks, such as face recognition and emotion recognition, multimodal information is used for accurate classification. Once a multimodal classification model is trained with a set of modalities, it estimates the class label by using the ...
Vijay John, Yasutomo Kawanishi
doaj +1 more source
Category Learning Through Multimodality Sensing [PDF]
Humans and other animals learn to form complex categories without receiving a target output, or teaching signal, with each input pattern. In contrast, most computer algorithms that emulate such performance assume the brain is provided with the correct output at the neuronal level or require grossly unphysiological methods of information propagation ...
Virginia R. de Sa, Dana H. Ballard
openaire +2 more sources
Value of MRI Outcomes for Preventive and Early‐Stage Trials in Spinocerebellar Ataxias 1 and 3
ABSTRACT Objective To examine the value of MRI outcomes as endpoints for preventive and early‐stage trials of two polyglutamine spinocerebellar ataxias (SCAs). Methods A cohort of 100 participants (23 SCA1, 63 SCA3, median Scale for the Assessment and Rating of Ataxia (SARA) score = 5, 42% preataxic, and 14 gene‐negative controls) was scanned at 3T up ...
Thiago J. R. Rezende +26 more
wiley +1 more source
IntroductionAssociating multimodal information is essential for human cognitive abilities including mathematical skills. Multimodal learning has also attracted attention in the field of machine learning, and it has been suggested that the acquisition of ...
Kamma Noda +2 more
doaj +1 more source
ABSTRACT Background Emerging evidence suggests that low‐frequency neural oscillations are dynamically regulated by consciousness levels, with the recovery of low cortical activity potentially serving as a neurophysiological substrate for conscious emergence. Targeted enhancement of these low‐frequency rhythms in patients with disorders of consciousness
Chuan Xu +10 more
wiley +1 more source

