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The role of fMRI in the mind decoding process in adults: a systematic review.
PeerJ
January 2025
Faculty of Graduate Studies, Daffodil International University, Dhaka, Dhaka, Bangladesh.
Background: Functional magnetic resonance imaging (fMRI) has revolutionized our understanding of brain activity by non-invasively detecting changes in blood oxygen levels. This review explores how fMRI is used to study mind-reading processes in adults.
Methodology: A systematic search was conducted across Web of Science, PubMed, and Google Scholar.
Personalized μ-transcranial alternating current stimulation improves online brain-computer interface control.
J Neural Eng
January 2025
ECE & Neurology, University of Texas at Austin, 301 E. Dean Keeton St. C2100, Austin, Texas, 78712-1139, UNITED STATES.
Objective: A motor imagery (MI)-based brain-computer interface (BCI) enables users to engage with external environments by capturing and decoding electroencephalography (EEG) signals associated with the imagined movement of specific limbs. Despite significant advancements in BCI technologies over the past 40 years, a notable challenge remains: many users lack BCI proficiency, unable to produce sufficiently distinct and reliable MI brain patterns, hence leading to low classification rates in their BCIs. The objective of this study is to enhance the online performance of MI-BCIs in a personalized, biomarker-driven approach using transcranial alternating current stimulation (tACS).
View Article and Find Full Text PDFMulti-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network for ERP Detection.
Neural Netw
January 2025
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China; Center of Intelligent Computing, School of Mathematics, East China University of Science and Technology, Shanghai 200237, China. Electronic address:
Event-related potentials (ERPs) can reveal brain activity elicited by external stimuli. Innovative methods to decode ERPs could enhance the accuracy of brain-computer interface (BCI) technology and promote the understanding of cognitive processes. This paper proposes a novel Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network (MS-PSA-SOC) for ERP Detection.
View Article and Find Full Text PDFOptimizing the proportion of stimulation area in a grid stimulus for user-friendly SSVEP-based BCIs.
J Neural Eng
January 2025
Institute of Semiconductors Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, 100083, CHINA.
Objective: Steady-state visual evoked potentials (SSVEPs) rely on the photic driving response to encode electroencephalogram (EEG) signals stably and efficiently. However, the user experience of the traditional stimulation with high-contrast flickers urgently needs to be improved. In this study, we introduce a novel paradigm of grid stimulation with weak flickering perception, distinguished by a markedly lower proportion of stimulation area in the overall pattern.
View Article and Find Full Text PDFDecoding speech intent from non-frontal cortical areas.
J Neural Eng
January 2025
Department of Neurology, Northwestern University Feinberg School of Medicine, 320 East Superior St, Chicago, IL 60611, USA, Chicago, Illinois, 60611, UNITED STATES.
Brain-machine interfaces (BMIs) have advanced greatly in decoding speech signals originating from the speech motor cortices. Primarily, these BMIs target individuals with intact speech motor cortices but who are paralyzed by disrupted connections between frontal cortices and their articulators due to brainstem stroke or motor neuron diseases such as amyotrophic lateral sclerosis. A few studies have shown some information outside the speech motor cortices, such as in parietal and temporal lobes, that also may be useful for BMIs.
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