Search Results (7)

Background/Objectives: The Implicit Prosody Hypothesis (IPH) posits that individuals generate internal prosodic representations during silent reading, mirroring those produced in spoken language. While converging behavioral evidence supports the IPH, the underlying neurocognitive mechanisms remain largely unknown. Therefore, this study investigated the neurophysiological markers of sensitivity to speech rhythm cues during silent word reading. Methods: EEGs were recorded while participants silently read four-word sequences, each composed of either trochaic words (stressed on the first syllable) or iambic words (stressed on the second syllable). Each sequence was followed by a target word that was either metrically congruent or incongruent with the preceding rhythmic pattern. To investigate the effects of metrical expectancy and lexical stress type, we examined single-trial event-related potentials (ERPs) and time–frequency representations (TFRs) time-locked to target words. Results: The results showed significant differences based on the stress pattern expectancy and type. Specifically, words that carried unexpected stress elicited larger ERP negativities between 240 and 628 ms after the word onset. Furthermore, different frequency bands were sensitive to distinct aspects of the rhythmic structure in language. Alpha activity tracked the rhythmic expectations, and theta and beta activities were sensitive to both the expected rhythms and specific locations of the stressed syllables. Conclusions: The findings clarify neurocognitive mechanisms of phonological and lexical mental representations during silent reading using a conservative data-driven approach. Similarity with neural response patterns previously reported for spoken language contexts suggests shared neural networks for implicit and explicit speech rhythm processing, further supporting the IPH and emphasizing the centrality of prosody in reading. Full article

In this work, we focus on silent speech recognition in electroencephalography (EEG) data of healthy individuals to advance brain–computer interface (BCI) development to include people with neurodegeneration and movement and communication difficulties in society. Our dataset was recorded from 270 healthy subjects during silent speech of eight different Russia words (commands): ‘forward’, ‘backward’, ‘up’, ‘down’, ‘help’, ‘take’, ‘stop’, and ‘release’, and one pseudoword. We began by demonstrating that silent word distributions can be very close statistically and that there are words describing directed movements that share similar patterns of brain activity. However, after training one individual, we achieved 85% accuracy performing 9 words (including pseudoword) classification and 88% accuracy on binary classification on average. We show that a smaller dataset collected on one participant allows for building a more accurate classifier for a given subject than a larger dataset collected on a group of people. At the same time, we show that the learning outcomes on a limited sample of EEG-data are transferable to the general population. Thus, we demonstrate the possibility of using selected command-words to create an EEG-based input device for people on whom the neural network classifier has not been trained, which is particularly important for people with disabilities. Full article

Topics: Functional Magnetic Resonance Imaging (fMRI) evaluation of HyberBaric Oxygen Therapy (HBOT) effects on chronic cerebral stroke Patients (Pts). Introduction: Our aim was to evaluate with fMRI, in a 3 Tesla system, the functional effects of HBOT on the Central Nervous System (CNS) in four Pts with established ischaemic and haemorrhagic cerebral strokes (2 Pts each). To our knowledge, no author used fMRI technique for this purpose, till now. Methods: All four Pts underwent a fMRI study before and after 40 HBOT sessions, with a time window of a few days. They carried out two language (text listening, silent word-verb generation) and two motor (hand and foot movements) tasks (30 s On-Off block paradigms). Results: After HBOT, all Pts reported a clinical improvement, mostly concerning language fluency and motor paresis. fMRI analysis demonstrated an increase in both the extent and the statistical significance of most of the examined eloquent areas. Conclusions: These changes were consistent with the clinical improvement in all Pts, suggesting a possible role of fMRI in revealing neuronal functional correlates of neuronal plasticity and HBOT-related neoangiogenesis. Although only four Pts were examined, fMRI proved to be a sensitive, non-invasive and reliable modality for monitoring neuronal functional changes before and after HBOT. Full article

Making decisions regarding return-to-play after sport-related concussion (SRC) based on resolution of symptoms alone can expose contact-sport athletes to further injury before their recovery is complete. Task-related functional near-infrared spectroscopy (fNIRS) could be used to scan for abnormalities in the brain activation patterns of SRC athletes and help clinicians to manage their return-to-play. This study aims to show a proof of concept of mapping brain activation, using tomographic task-related fNIRS, as part of the clinical assessment of acute SRC patients. A high-density frequency-domain optical device was used to scan 2 SRC patients, within 72 h from injury, during the execution of 3 neurocognitive tests used in clinical practice. The optical data were resolved into a tomographic reconstruction of the brain functional activation pattern, using diffuse optical tomography. Moreover, brain activity was inferred using single-subject statistical analyses. The advantages and limitations of the introduction of this optical technique into the clinical assessment of acute SRC patients are discussed. Full article

Silent speech decoding is a novel application of the Brain–Computer Interface (BCI) based on articulatory neuromuscular activities, reducing difficulties in data acquirement and processing. In this paper, spatial features and decoders that can be used to recognize the neuromuscular signals are investigated. Surface electromyography (sEMG) data are recorded from human subjects in mimed speech situations. Specifically, we propose to utilize transfer learning and deep learning methods by transforming the sEMG data into spectrograms that contain abundant information in time and frequency domains and are regarded as channel-interactive. For transfer learning, a pre-trained model of Xception on the large image dataset is used for feature generation. Three deep learning methods, Multi-Layer Perception, Convolutional Neural Network and bidirectional Long Short-Term Memory, are then trained using the extracted features and evaluated for recognizing the articulatory muscles’ movements in our word set. The proposed decoders successfully recognized the silent speech and bidirectional Long Short-Term Memory achieved the best accuracy of 90%, outperforming the other two algorithms. Experimental results demonstrate the validity of spectrogram features and deep learning algorithms. Full article

Given the high proportion of struggling readers in school and the long-term negative consequences of underachievement for those affected, the question of prevention options arises. The early identification of central indicators for reading literacy is a noteworthy starting point. In this context, curriculum-based measurements have established themselves as reliable and valid instruments for monitoring the progress of learning processes. This article is dedicated to the assessment of word recognition in silent reading as an indicator of adequate reading fluency. The process of developing an item pool is described, from which instruments for learning process diagnostics can be derived. A sample of 4268 students from grades 1–4 processed a subset of items. Each student template included anchor items, which all students processed. Using Item Response Theory, item statistics were estimated for the entire sample and all items. After eliminating unsuitable items (N = 206), a one-dimensional, homogeneous pool of items remained. In addition, there are high correlations with another established reading test. This provides the first evidence that the recording of word recognition skills for silent reading can be seen as an economic indicator for reading skills. Although the item pool forms an important basis for the extraction of curriculum-based measurements, further investigations to assess the diagnostic suitability (e.g., the measurement invariance over different test times) are still pending. Full article

A similarity between the form and meaning of a word (i.e., iconicity) may help language users to more readily access its meaning through direct form-meaning mapping. Previous work has supported this view by providing empirical evidence for this facilitatory effect in sign language, as well as for onomatopoetic words (e.g., cuckoo) and ideophones (e.g., zigzag). Thus, it remains largely unknown whether the beneficial role of iconicity in making semantic decisions can be considered a general feature in spoken language applying also to “ordinary” words in the lexicon. By capitalizing on the affective domain, and in particular arousal, we organized words in two distinctive groups of iconic vs. non-iconic based on the congruence vs. incongruence of their lexical (meaning) and sublexical (sound) arousal. In a two-alternative forced choice task, we asked participants to evaluate the arousal of printed words that were lexically either high or low arousing. In line with our hypothesis, iconic words were evaluated more quickly and more accurately than their non-iconic counterparts. These results indicate a processing advantage for iconic words, suggesting that language users are sensitive to sound-meaning mappings even when words are presented visually and read silently. Full article