A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application

© 2016 IEEE. A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications

Optimizacija upravljanja brzinom mobilnog robota s izbjegavanjem prepreka zasnovana na teoriji vijabilnosti

The navigation efficiency of wheeled robots needs to be further improved. Although related research has proposed various approaches, most of them describe the relationship between the robot and the obstacle roughly. Viability theory concerns the dynamic adaptation of evolutionary systems to the environment. Based on viability, we explore a method that involves robot dynamic model, environmental constraints and navigation control. The method can raise the efficiency of the navigation. We treat the environment as line segments to reduce the computational difficulty for building the viability condition constraints. Although there exists lots of control values which can be used to drive the robot safely to the goal, it is necessary to build an optimization model to select a more efficient control value for the navigation. Our simulation shows that viability theory can precisely describe the link between robotic dynamics and the obstacle, and thus can help the robot to achieve radical high speed navigation in an unknown environment.Postoji potreba za unaprijeđenjem učinkovitosti navigacije mobilnih robota. Iako su vezana istraživanja predložila različite pristupe, većina ne opisuje precizno odnos između robota i prepreke. Teorija vijabilnosti istražuje dinamičke adaptacije evolucijskih sustava njihovoj okolini. U članku istražujemo metodu koja može povećati učinkovitost navigacije, zasnovanu na vijabilnosti koja uključuje dinamički model robota, ograničenja okoline robota i samu navigaciju. Radna okolina predstavljena je ravnim crtama kako bi se smanjila računska složenost izgradnje ograničenja. Iako postoji veliki broj iznosa upravljačkih veličina koje bi sigurno uputile robota prema cilju, potrebno je izraditi optimizacijski model koji bi odabrao učinkovitiju upravljačku vrijednost za navigaciju. Izvedene simulacije pokazuju da teorija vijabilnosti može precizno opisati vezu između prepreke i dinamike robota te na taj način pomoći robotu da postigne radikalno veće brzine pri navigaciji u nepoznatim prostorima

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available