Faculty Profiles - TANAKA Yuichiro

Papers - TANAKA Yuichiro

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Inner Speech Recognition Using EEG Signals with Reservoir Computing Reviewed International journal

Syulistyo Arie Rachmad, Tanaka Yuichiro, Tamukoh Hakaru

International Journal of Activity and Behavior Computing ( Care XDX Center, Kyushu Institute of Technology ) 2025 ( 2 ) 1 - 19 2025.06

Language：English Publishing type：Research paper (scientific journal)

Inner speech recognition using electroencephalography (EEG) signals holds significant potential for advancing brain-computer interface (BCI), particularly for individuals with speech impairments. However, decoding inner speech from EEG data remains a challenging task due to the nonlinear, high-dimensional and temporal dynamic nature of neural signals. In order to address the challenges, this study explores the application of reservoir computing (RC), with a particular focus on bidirectional RC, for the purpose of classifying inner speech from EEG signals. Contrary to unidirectional RC, which processes data in a single time direction, bidirectional RC captures both past and future temporal dependencies. This enhancement of the extraction of meaningful EEG features for classification is a significant contribution of this study. We evaluate the performance of both unidirectional and bidirectional RC architectures across a range of reservoir sizes (400, 500, and 600 units) to identify the most effective configuration for this task. Our results demonstrate that the bidirectional RC consistently outperforms the unidirectional RC in terms of accuracy and F1-score across all reservoir sizes, highlighting its superior ability to extract comprehensive temporal features from EEG data. The optimal performance is achieved by bidirectional RC with 600 reservoir units, yielding an accuracy of 18.94% and an F1-score of 19.02%, which surpasses all other configurations. In contrast, unidirectional RC with 600 units exhibits a lower accuracy of 17.22%. These findings underscore the potential of bidirectional RC in EEG signals classification for inner speech recognition, offering a promising direction for developing efficient BCI with low-cost computation.

DOI： 10.60401/ijabc.117

CiNii Research

Prediction of Timing and Amount of Houseplants Watering by an Echo State Network on Jetson Reviewed International journal

Yoshimura W., Arimura K., Kobayashi R., Mizutani A., Fujino T., Tanaka Y., Sudo T., Ishizuka N., Ito K., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 529 - 534 2025.01

Lightweight Hand-Waving Action Recognition Using Reservoir Computing in a Cafeteria Environment Reviewed

Isomoto K., Fumoto S., Kobayashi R., Tanaka Y., Tamukoh H.

2025 IEEE/SICE International Symposium on System Integration, SII 2025 1034 - 1040 2025.01

Classification of Human Activity by Spiking Neural Networks using Event-based Vision Sensors Reviewed

Rakheja A., Saini R., Toyoda R., Tanaka Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 484 - 486 2025.01

Classification of Human Activity by Event-based Vision Sensors using Echo State Networks Reviewed International journal

Saini R., Rakheja A., Toyoda R., Tanaka Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 476 - 478 2025.01

Application of AI Robot Technology for Biophilic Design Reviewed International journal

Manabe K., Miyazono R., Ito K., Sudo T., Ishizuka N., Mizutani A., Tamukoh H., Tanaka Y., Ueda E., Anamizu Y., Furuya L., Tamai H., Nakano S., Tanaka H.

Proceedings of International Conference on Artificial Life and Robotics 513 - 518 2025.01

An Exhibition Environment with 2D Markers for Guide Robot Reviewed International journal

Mizutani A., Hattori Y., Tanaka Y., Ishizuka N., Tanaka H., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 524 - 528 2025.01

Hibikino-Musashi@Home RoboCup@Home DSPL Champion 2024 International journal

Mizutani A., Isomoto K., Yamao K., Kobayashi R., Fumoto S., Arimura K., Yamaguchi N., Shiba T., Kimizuka K., Ohno Y., Terashima R., Yamaguchi H., Fujino T., Maruno R., Yoshimura W., Mine K., Phu Thien Nhan T., Yano Y., Tanaka Y., Nishida T., Morie T., Tamukoh H.

Lecture Notes in Computer Science 15570 LNAI 461 - 472 2025.01

Creating a Future Space Where Humans and Robots Live Together

Mizutani Akinobu, Ito Keitaro, Ishizuka Naoto, Sudo Tomomi, Tanaka Hirofumi, Tanaka Yuichiro, Tamukoh Hakaru

Journal of the Robotics Society of Japan ( The Robotics Society of Japan ) 43 ( 4 ) 367 - 370 2025.01

Simple open-set recognition combining metric learning and anomaly detection Reviewed International journal

Kanaoka D., Tanaka Y., Tamukoh H.

Advanced Robotics 39 ( 7 ) 368 - 382 2025.01

Object recognition and grasping point detection using carbon nanotube - polydimethylsiloxane nanocomposite sensor Reviewed International journal

Tokuno Shoshi, Kimizuka Kouki, Tanaka Yuichiro, Usami Yuki, Tanaka Hirofumi, Tamukoh Hakaru

Nonlinear Theory and Its Applications, IEICE ( The Institute of Electronics, Information and Communication Engineers ) 15 ( 4 ) 883 - 898 2024.10

Language：English Publishing type：Research paper (scientific journal)

This paper presents a system for processing tactile information using a carbon nanotube (CNT)-polydimethylsiloxane (PDMS) nanocomposite sensor designed for robotic applications. This study introduces an approach for recognizing objects and detecting optimal grasping points using tactile data from a sensor-equipped robotic hand. The sensor is expected to be more efficient than a computerized implementation using sensor dynamics directly in the computation. The experiments demonstrated the system's ability to classify nine types of objects with an accuracy of 83.56% and to discern two grasping points on four object types, achieving a 71.7% success rate.

DOI： 10.1587/nolta.15.883

Scopus

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/nolta/15/4/15_883/_pdf

Physical Reservoir Computing Devices Utilizing Nanomaterial Random Network and Its Sensing Application Invited Reviewed

TANAKA Hirofumi, AZHARI Saman, BANERJEE Deep, KIMIZUKA Kouki, TANAKA Yuichiro, USAMI Yuki

Vacuum and Surface Science ( The Japan Society of Vacuum and Surface Science ) 67 ( 9 ) 428 - 432 2024.09

Language：Japanese Publishing type：Research paper (scientific journal)

The rapid advancement of software-based deep learning has led to a surge in AI applications, yet hardware limitations in silicon CMOS technology hinder performance. Consequently, interest is growing in hardware technologies and new materials for artificial neural networks (ANNs) and neuromorphic systems. Exploiting nanomaterials nonlinearity caused by spontaneous physical phenomena holds promise of reducing power consumption in AI hardware. Reservoir computing devices, derived from recurrent neural networks, play a crucial role, with material reservoir devices showcasing “material intelligence.” Tailored nanomaterials for reservoir devices hold potential for revolutionizing AI, especially in robotics. As research progresses, focusing on device functionalization and applications, recent findings underline the significance of integrating nanomaterials into AI hardware for enhanced computational capabilities and energy efficiency.

DOI： 10.1380/vss.67.428

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/vss/67/9/67_20181334/_pdf

A Hippocampus-Inspired Environment-Specific Knowledge Acquisition System Utilizing Common Knowledge with Contextual Information Reviewed International journal

Mizutani A., Tanaka Y., Tamukoh H., Nomura O., Tateno K., Morie T.

Proceedings of the International Joint Conference on Neural Networks 2024.07

Reservoir-based 1D convolution: low-training-cost AI Reviewed International journal

TANAKA Yuichiro, TAMUKOH Hakaru

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences ( The Institute of Electronics, Information and Communication Engineers ) advpub ( 0 ) 941 - 944 2024.06

Authorship：Lead author,　Corresponding author Language：English Publishing type：Research paper (scientific journal)

In this study, we introduce a reservoir-based one-dimensional (1D) convolutional neural network that processes time-series data at a low computational cost, and investigate its performance and training time. Experimental results show that the proposed network consumes lower training computational costs and that it outperforms the conventional reservoir computing in a sound-classification task.

DOI： 10.1587/transfun.2023eal2050

Scopus

CiNii Research

Other Link： https://www.jstage.jst.go.jp/article/transfun/advpub/0/advpub_2023EAL2050/_pdf

Recognizing Nursing Activities in Endotracheal Suction: Utilizing Multiple Readouts Reservoir Computing and Large Language Models Reviewed International journal

Arie Rachmad Syulistyo, Yuichiro Tanaka, Hakaru Tamukoh

6th International Conference on Activity and Behavior Computing 2024.05

Enhancing Memory Capacity of Reservoir Computing with Delayed Input and Efficient Hardware Implementation with Shift Registers Reviewed International journal

Hirayae S., Yoshioka K., Yokota A., Kawashima I., Tanaka Y., Katori Y., Nomura O., Morie T., Tamukoh H.

Proceedings - IEEE International Symposium on Circuits and Systems 2024.05

Development of A SayCan-based Task Planning System Capable of Handling Abstract Nouns Reviewed International journal

Yamao K., Kanaoka D., Isomoto K., Mizutani A., Tanaka Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 430 - 434 2024.02

A Low Computational Cost Hand Waving Action Recognition System with Echo State Network for Home Service Robots Reviewed International journal

Yamaguchi H., Mizutani A., Syulistyo A.R., Tanaka Y., Tamukoh H.

Proceedings of International Conference on Artificial Life and Robotics 421 - 424 2024.02

Object recognition using CNT-PDMS nanocomposite sensor on a robot hand Reviewed

Shoshi Tokuno, Kouki Kimizuka, Yuichiro Tanaka, Hirofumi Tanaka, Hakaru Tamukoh

The 22nd International Symposium on Eco-materials Processing and Design 2024.01

Thermally Stable Ag<inf>2</inf>Se Nanowire Network as an Effective In-Materio Physical Reservoir Computing Device Reviewed International journal

Kotooka T., Lilak S., Stieg A.Z., Gimzewski J.K., Sugiyama N., Tanaka Y., Kawabata T., Karacali A., Tamukoh H., Usami Y., Tanaka H.

Advanced Electronic Materials 10 ( 12 ) 2024.01

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Papers - TANAKA Yuichiro