Varactor-Based Tunable Planar Filters and Post-Fabrication Tuning of Microwave Filters

Post-fabrication tuning of filters is usually realized by adding number of elements for tuning the frequency and/or controlling the couplings between the resonators. The task of these tuning elements is to control resonators center frequency, inter-resonators coupling and input/output couplings. While the most common tool for the post-fabrication tuning is to use tuning screws and rods, it is not usually practical to tune a planar filter with these tools. This thesis introduces a novel method for global post-fabrication tuning of microwave filters by designing and adding a passive distributed-element circuit in parallel to the detuned filter. The idea, which is demonstrated by experimental results, has several advantages over traditional techniques for filter tuning that use screws. The quality factor of resonator reduces significantly after adding the tuning screws while the proposed method does not affect the Q of resonators. The most important advantage of the proposed compensator circuit is that it can be employed without knowing details of the detuned filters. Since the compensator circuit will be added in parallel to the detuned filter, it will not affect the elements of filter individually. So whether the filter is planar or cavity, the proposed circuit can be used for the tuning. The experimental results obtained demonstrate the validity of this method. The dissertation also presents a novel concept for designing a center frequency and bandwidth tunable microstrip filter by using GaAs varactors. The proposed isolated coupling structure which is used in this filter makes the bandwidth tuning possible by reducing the loading effect of coupling elements on the resonators. The center frequency of this filter can be also tuned by using a different set of varactors connected to resonators. A 3-pole filter based on this concept has been designed and simulated. The concept can be expanded to higher order filters

Neurolinguistics Research Advancing Development of a Direct-Speech Brain-Computer Interface

A direct-speech brain-computer interface (DS-BCI) acquires neural signals corresponding to imagined speech, then processes and decodes these signals to produce a linguistic output in the form of phonemes, words, or sentences. Recent research has shown the potential of neurolinguistics to enhance decoding approaches to imagined speech with the inclusion of semantics and phonology in experimental procedures. As neurolinguistics research findings are beginning to be incorporated within the scope of DS-BCI research, it is our view that a thorough understanding of imagined speech, and its relationship with overt speech, must be considered an integral feature of research in this field. With a focus on imagined speech, we provide a review of the most important neurolinguistics research informing the field of DS-BCI and suggest how this research may be utilized to improve current experimental protocols and decoding techniques. Our review of the literature supports a cross-disciplinary approach to DS-BCI research, in which neurolinguistics concepts and methods are utilized to aid development of a naturalistic mode of communication. : Cognitive Neuroscience; Computer Science; Hardware Interface Subject Areas: Cognitive Neuroscience, Computer Science, Hardware Interfac

A Ternary Brain-Computer Interface Based on Single-Trial Readiness Potentials of Self-initiated Fine Movements: A Diversified Classification Scheme

In recent years, the readiness potential (RP), a type of pre-movement neural activity, has been investigated for asynchronous electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Since the RP is attenuated for involuntary movements, a BCI driven by RP alone could facilitate intentional control amid a plethora of unintentional movements. Previous studies have mainly attempted binary single-trial classification of RP. An RP-based BCI with three or more states would expand the options for functional control. Here, we propose a ternary BCI based on single-trial RPs. This BCI classifies amongst an idle state, a left hand and a right hand self-initiated fine movement. A pipeline of spatio-temporal filtering with per participant parameter optimization was used for feature extraction. The ternary classification was decomposed into binary classifications using a decision-directed acyclic graph (DDAG). For each class pair in the DDAG structure, an ordered diversified classifier system (ODCS-DDAG) was used to select the best among various classification algorithms or to combine the results of different classification algorithms. Using EEG data from 14 participants performing self-initiated left or right key presses, punctuated with rest periods, we compared the performance of ODCS-DDAG to a ternary classifier and four popular multiclass decomposition methods using only a single classification algorithm. ODCS-DDAG had the highest performance (0.769 Cohen's Kappa score) and was significantly better than the ternary classifier and two of the four multiclass decomposition methods. Our work supports further study of RP-based BCI for intuitive asynchronous environmental control or augmentative communication