Research papers on brain computer interfaces published today:
- by Daohan ZhangHere, we present a protocol to decode Mandarin sentences from invasive neural recordings using a brain-to-text framework. We describe steps for preparing materials, including designing the sentence corpus and setting up electrocorticography (ECoG) recording systems. We then detail procedures for decoding, such as data preprocessing, selection of speech-responsive electrodes, speech detection, syllable and tone decoding, and language modeling. We also outline performance evaluation metrics. For…
- by Yuan LiuMotor dysfunction is one of the most significant sequelae of stroke, with lower limb impairment being a major concern for stroke patients. Motor imagery (MI) technology based on brain-computer interface (BCI) offers promising rehabilitation potential for stroke patients by activating motor-related brain areas. However, developing a robust BCI-MI system and uncovering the underlying mechanisms of neural plasticity during stroke recovery through such systems requires large-scale datasets. These…
- by Wenzhe LiaoCONCLUSION: Experimental results demonstrate that the proposed CIACNet model has strong classification capabilities and low time cost. Removing one or more blocks results in a decline in the overall performance of the model, indicating that each block within the model makes a significant contribution to its overall effectiveness. These results demonstrate the ability of the CIACNet model to reduce time costs and improve performance in motor imagery brain-computer interface (MI-BCI) systems,…
- by Yuhao CaiClinically available therapies often inadequately address severe chronic cutaneous pain due to short anesthetic duration, insufficient intensity, or side effects. This study introduces a pen device delivering tetrodotoxin (TTX), a potent neurotoxin targeting nerve voltage-gated sodium channels, as a safe and effective topical anesthetic to treat severe chronic cutaneous pain. Chemical permeation enhancers, such as sodium dodecyl sulfate (SDS) and limonene (LIM), are incorporated to enhance TTX…
- by Suhail T ACONCLUSIONS: The experimental results on the effect of NFT in patient groups demonstrated that the proposed neurofeedback game has the potential to enhance attention and memory skills in patients with neurological disorders. A large-scale study is needed in the future to prove the efficacy on a wider population.
- by Minjie WangCONCLUSIONS: The fully laser-micromachined soft neural interface device with color adjusted PDMS encapsulation layer shows great promise for applications in SCS.
- by Liangyu LiCONCLUSION: The accuracy of artificial intelligence systems is over 70%. This study provides clinical doctors with a new precision medicine strategy and tool to regulate patient behavior and reduce disease risk. Other: This project was approved by the Ethics Committee of Chifeng Cancer Hospital and reported to the WHO.
- by George F HoeferlinBrain-machine interface performance can be affected by neuroinflammatory responses due to blood-brain barrier (BBB) damage following intracortical microelectrode implantation. Recent findings suggest that certain gut bacterial constituents might enter the brain through damaged BBB. Therefore, we hypothesized that damage to the BBB caused by microelectrode implantation could facilitate microbiome entry into the brain. In our study, we found bacterial sequences, including gut-related ones, in the…
- by Ekansh GuptaBrain-computer interfaces (BCIs) offer an implicit, non-linguistic communication channel between users and machines. Despite their potential, BCIs are far from becoming a mainstream communication modality like text and speech. While non-invasive BCIs, such as Electroencephalography, are favored for their ease of use, their broader adoption is limited by challenges related to signal noise, artifacts, and variability across users. In this paper, we propose a novel method called response coupling,…
- by Kinkini BhadraBrain-Computer Interfaces (BCI) will revolutionize the way people with severe impairment of speech production can communicate. While current efforts focus on training classifiers on vast amounts of neurophysiological signals to decode imagined speech, much less attention has been given to users' ability to adapt their neural activity to improve BCI-control. To address whether BCI-control improves with training and characterize the underlying neural dynamics, we trained 15 healthy participants to…
- by Irma Nayeli Angulo-ShermanCONCLUSIONS: Motor imagery proficiency is related to the focused and lateralized event-related α desynchronization patterns and the lateralization of β and γ PDC. Future analysis of these features could allow complimenting the information for assessment of subject-specific BCI control and the prediction of the effectiveness of motor-imagery training.
- by Venkatesh KanagaluruCONCLUSION: This study demonstrates that SSVEP classification using machine-learning models improves accuracy and efficiency. The use of wavelet decomposition for feature extraction and machine learning for classification offers a robust method for SSVEP-based BCIs. This method is promising for assistive technologies and other BCI applications.
- by Mervyn Jun Rui LimObjective Invasive brain-computer interfaces (iBCIs) have evolved significantly since the first neurotrophic electrode was implanted in a human subject three decades ago. Since then, both hardware and software advances have increased the iBCI performance to enable tasks such as decoding conversations in real-time and manipulating external limb prostheses with haptic feedback. In this systematic review, we aim to evaluate the advances in iBCI hardware, software and functionality and describe…
- by S S MagalhãesThe human brain demonstrates an exceptional adaptability, which encompasses the ability to regulate emotions, exhibit cognitive flexibility, and generate behavioral responses, all supported by neuroplasticity. Brain-computer interfaces (BCIs) employ adaptive algorithms and machine learning techniques to adapt to variations in the user's brain activity, allowing for customized interactions with external devices. Older adults may experience cognitive decline, which could affect the ability to…
- by Anna Latha MPrefrontal Cortex-Directional Rhythms (PFC-DR) classification plays a significant role in Brain-Computer Interface (BCI) research since it is crucial for the effective rehabilitation of injured voluntary movements. The primary aims of this study are to conduct a thorough examination of traditional classification techniques, while emphasizing the significance of radial basis functions within support vector machine (RBF-SVM) based approaches in the context of BCI systems. Consequently, in contrast…
- by Nan WangCONCLUSION: As a noninvasive, portable, and real-time neuroimaging tool, fNIRS holds significant promise for advancing the assessment and treatment of DoC. Despite limitations such as low spatial resolution and the need for standardized protocols, fNIRS has demonstrated its utility in evaluating residual brain activity, detecting covert consciousness, and monitoring therapeutic interventions. In addition to assessing consciousness levels, fNIRS offers unique advantages in tracking hemodynamic…
- by Babu ChintaElectroencephalogram (EEG) signals enhance human-machine interaction but pose challenges in speech recognition due to noise and complexity. This study proposes an Efficient Deep Learning Approach (EDLA) integrating the Gannet Optimization Algorithm (GOA) and Elman Recurrent Neural Network (ERNN) for speaker identification. EEG data is preprocessed using a Savitzky-Golay filter, and key features are selected via recursive feature elimination. Evaluated on the Kara One dataset, EDLA achieves 95.2%…
- by Gauttam JangirHuman brain signal processing and finger's movement coordination is a complex mechanism. In this mechanism finger's movement is mostly performed for every day's task. It is well known that to capture such movement EEG or ECoG signals are used. In this order to find the patterns from these signals is important. The BCI competition 4 dataset 4 is one such standard dataset of ECoG signals for individual finger movement provided by University of Washington, USA. In this work, this dataset is,…
- by Zhining ZhouOBJECTIVE: The brain-computer interface (BCI) is currently experiencing a surge in the number of recording channels, resulting in a vast amount of raw data. It has become crucial to reliably detect neural spikes from a large population of neurons in the presence of noise, in order to constrain the transmission bandwidth.
- by Li ZhuTraditional motor imagery-based single-brain computer interfaces(BCIs) face inherent limitations, such as unstable signals and low recognition accuracy. In contrast, multi-brain BCIs offer a promising solution by leveraging group electroencephalography (EEG) data. This paper presents a novel multi-layer EEG fusion method with channel selection for motor imagery-based multi-brain BCIs. We utilize mutual information convergent cross-mapping (MCCM) to identify channels that the represent causal…