A brain–computer interface (BCI) enables direct communication between the brain and external devices by translating neural activity into executable control commands. Among electroencephalography (EEG)-based paradigms, steady-state visual evoked potential (SSVEP) is widely adopted due to its high signal-to-noise ratio, robustness, and minimal calibration requirements. While SSVEP-based spellers have been extensively investigated, many existing systems rely on high-channel-density EEG recordings and computationally complex processing pipelines, and are primarily designed for alphabetic input structures. In this study, we present an SSVEP-based Korean speller that integrates the Cheonjiin keyboard layout to support intuitive composition of Hangul syllables. The proposed system adopts a simple configuration, employing only five visual stimulation frequencies (6.67–12 Hz) and two occipital EEG channels (O1 and O2), with real-time frequency recognition performed using canonical correlation analysis (CCA) within a 1.5 s sliding window. EEG signals were acquired at 200 Hz using an OpenBCI Ganglion board, band-pass filtered (5–45 Hz), and processed with harmonic sinusoidal reference templates for multi-frequency classification. The proposed interface generates five control commands (up, down, left, right, and select), enabling directional cursor navigation and character confirmation on a 4 × 4 virtual Cheonjiin keyboard. Experimental validation with three healthy participants demonstrated an average classification accuracy of approximately 82% and an information transfer rate (ITR) of 31.2 bits/min. Frequency-domain analysis revealed clear spectral peaks at the stimulation frequencies and their harmonics, indicating reliable SSVEP responses. The proposed system employs a simple two-channel configuration integrated with a Korean language-specific input structure, demonstrating that reliable SSVEP-based communication can be realized without computationally intensive algorithms or high-cost EEG acquisition systems. These findings demonstrate that reliable SSVEP-based communication can be achieved using a low-channel configuration without reliance on high-cost EEG equipment.

Our study introduces a surface electromyography (sEMG)-based Cheonjiin speller system developed to assist individuals with restricted hand mobility. The interface incorporates a directional control framework—comprising up, down, left, right, and select commands—integrated with a Korean keyboard layout to enable efficient and accessible text input. Two-channel surface EMG signals were recorded from the rectus femoris and gastrocnemius muscles at a sampling rate of 200 Hz using an EMG acquisition module. The signals were processed in real time using notch and bandpass filtering, followed by full-wave rectification. To decode user intent, three physiologically interpretable time-domain features—root mean square (RMS), slope sign change (SSC), and peak amplitude—were extracted and subsequently used for classification. The Cheonjiin speller was implemented in Python 3.10.8 and operated through directional cursor navigation. System performance was quantitatively evaluated in two experiments: in Experiment 1, recognition accuracy for five discrete commands reached an average of 90.0%, while Experiment 2, involving continuous Korean word and sentence input, achieved an average accuracy of 88.65%. Across both experimental conditions, the system attained an average information transfer rate (ITR) of 96.19 bits/min, confirming efficient real-time communication capability. The results demonstrate that high recognition performance can be achieved using simple, low-computation features without deep learning models, confirming the feasibility of real-time implementation in resource-limited environments. Overall, the proposed speller system exhibits high operability, accessibility, and practical usability in constrained conditions and holds potential for integration into augmentative and alternative communication (AAC) systems for users with motor impairments. Moreover, its lightweight architecture, minimal computational load, and flexible directional control structure make it adaptable to a wide range of assistive and wearable technology applications.

This study presents the development of a proof of concept, functional hydrogel interface designed for the acquisition of physiological signals, such as electrocardiogram (ECG) and electromyography (EMG). The hydrogel is synthesized using chitosan extracted from the shells of Tenebrio molitor larvae through a sustainable acid–alkaline protocol, blended with glycerol, polyvinyl alcohol (PVA), and ionized with NaCl to enhance conductivity. The resulting hydrogel membranes were cast and cut into circular shapes to provide a uniform contact geometry. The fabrication process yielded flexible membranes exhibiting ionic conductivity and partial surface conformity and handling stability. The extracted chitosan was characterized by Fourier-transform infrared spectroscopy (FTIR), degree of deacetylation (DDA), and molecular weight determination. Mechanical characterization included compression and tensile testing, while electrical characterization was performed through impedance spectroscopy and comparison with a commercial hydrogel interface. Functional evaluation was conducted through ECG and EMG signal acquisition under controlled experimental conditions. Preliminary in situ ECG and EMG recordings demonstrated successful signal acquisition using the proposed hydrogel interface. Future work may further investigate the mechanical and electrical behavior of the hydrogel under broader experimental conditions, as well as the optimization of the hydrogel formulation and extended physiological signal acquisition. Studies may help further characterize its potential as a chitosan-based bio interface material for bioelectrical sensing applications.

Cardiac auscultation remains a widely used non-invasive method for assessing cardiac function; however, conventional acoustic stethoscopes are limited by subjective interpretation and lack of digital signal-handling capabilities. This study presents the design and fabrication of a chitosan-based diaphragm digital stethoscope using a biopolymer-derived acoustic interface. Chitosan was extracted from mealworm larvae shells through sequential chemical processing and subsequently processed into a glycerol-plasticized film via solution casting to obtain a flexible diaphragm. The mechanical properties of the diaphragm were evaluated to assess its suitability for acoustic applications. The diaphragm was mechanically coupled to a piezoelectric sensor and integrated into a custom 3D-printed chest piece connected to a microcontroller-based acquisition system. Heart sound signals were acquired from four conventional auscultation sites (aortic, pulmonic, tricuspid, and mitral regions). The recorded signals were processed using band-pass filtering, envelope extraction, and time–frequency analysis to visualize waveform morphology and frequency content. The signals obtained exhibited temporal and spectral features consistent with reported phonocardiography characteristics, including identifiable S1 and S2 components. These results demonstrate the feasibility of using chitosan-based diaphragm materials for heart sound acquisition in a digital stethoscope configuration, providing a low-complexity platform for further development of biopolymer-based acoustic sensing devices.