Simplified interval type-2 fuzzy neural networks.

This paper describes a self-evolving interval type-2 fuzzy neural network (FNN) for various applications. As type-1 fuzzy systems cannot effectively handle uncertainties in information within the knowledge base, we propose a simple interval type-2 FNN, which uses interval type-2 fuzzy sets in the premise and the Takagi-Sugeno-Kang (TSK) type in the consequent of the fuzzy rule. The TSK-type consequent of fuzzy rule is a linear combination of exogenous input variables. Given an initially empty the rule-base, all rules are generated with on-line type-2 fuzzy clustering. Instead of the time-consuming K-M iterative procedure, the design factors ql and qr are learned to adaptively adjust the upper and lower positions on the left and right limit outputs, using the parameter update rule based on a gradient descent algorithm. Simulation results demonstrate that our approach yields fewer test errors and less computational complexity than other type-2 FNNs.

Adaptive neural-based fuzzy modeling for biological systems.

The inverse problem of identifying dynamic biological networks from their time-course response data set is a cornerstone of systems biology. Hill and Michaelis-Menten model, which is a forward approach, provides local kinetic information. However, repeated modifications and a large amount of experimental data are necessary for the parameter identification. S-system model, which is composed of highly nonlinear differential equations, provides the direct identification of an interactive network. However, the identification of skeletal-network structure is challenging. Moreover, biological systems are always subject to uncertainty and noise. Are there suitable candidates with the potential to deal with noise-contaminated data sets? Fuzzy set theory is developed for handing uncertainty, imprecision and complexity in the real world; for example, we say "driving speed is high" wherein speed is a fuzzy variable and high is a fuzzy set, which uses the membership function to indicate the degree of a element belonging to the set (words in Italics to denote fuzzy variables or fuzzy sets). Neural network possesses good robustness and learning capability. In this study we hybrid these two together into a neural-fuzzy modeling technique. A biological system is formulated to a multi-input-multi-output (MIMO) Takagi-Sugeno (T-S) fuzzy system, which is composed of rule-based linear subsystems. Two kinds of smooth membership functions (MFs), Gaussian and Bell-shaped MFs, are used. The performance of the proposed method is tested with three biological systems.

Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network.

This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems. The recurrent structure in an IRSFNN is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself. The consequent part in the IRSFNN is composed of a Takagi-Sugeno-Kang (TSK) or functional-link-based type. The proposed IRSFNN employs a functional link neural network (FLNN) to the consequent part of fuzzy rules for promoting the mapping ability. Unlike a TSK-type fuzzy neural network, the FLNN in the consequent part is a nonlinear function of input variables. An IRSFNNs learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning. An on-line clustering algorithm is effective in generating fuzzy rules. The consequent update parameters are derived by a variable-dimensional Kalman filter algorithm. The premise and recurrent parameters are learned through a gradient descent algorithm. We test the IRSFNN for the prediction and identification of dynamic plants and compare it to other well-known recurrent FNNs. The proposed model obtains enhanced performance results.

Investigation of the cerebral hemodynamic response function in single blood vessels by functional photoacoustic microscopy.

The specificity of the hemodynamic response function (HRF) is determined spatially by the vascular architecture and temporally by the evolution of hemodynamic changes. Here, we used functional photoacoustic microscopy (fPAM) to investigate single cerebral blood vessels of rats after left forepaw stimulation. In this system, we analyzed the spatiotemporal evolution of the HRFs of the total hemoglobin concentration (HbT), cerebral blood volume (CBV), and hemoglobin oxygen saturation (SO(2)). Changes in specific cerebral vessels corresponding to various electrical stimulation intensities and durations were bilaterally imaged with 36 × 65-µm(2) spatial resolution. Stimulation intensities of 1, 2, 6, and 10 mA were applied for periods of 5 or 15 s. Our results show that the relative functional changes in HbT, CBV, and SO(2) are highly dependent not only on the intensity of the stimulation, but also on its duration. Additionally, the duration of the stimulation has a strong influence on the spatiotemporal characteristics of the HRF as shorter stimuli elicit responses only in the local vasculature (smaller arterioles), whereas longer stimuli lead to greater vascular supply and drainage. This study suggests that the current fPAM system is reliable for studying relative cerebral hemodynamic changes, as well as for offering new insights into the dynamics of functional cerebral hemodynamic changes in small animals.

Design, simulation and experimental validation of a novel flexible neural probe for deep brain stimulation and multichannel recording.

An implantable micromachined neural probe with multichannel electrode arrays for both neural signal recording and electrical stimulation was designed, simulated and experimentally validated for deep brain stimulation (DBS) applications. The developed probe has a rough three-dimensional microstructure on the electrode surface to maximize the electrode-tissue contact area. The flexible, polyimide-based microelectrode arrays were each composed of a long shaft (14.9 mm in length) and 16 electrodes (5 µm thick and with a diameter of 16 µm). The ability of these arrays to record and stimulate specific areas in a rat brain was evaluated. Moreover, we have developed a finite element model (FEM) applied to an electric field to evaluate the volume of tissue activated (VTA) by DBS as a function of the stimulation parameters. The signal-to-noise ratio ranged from 4.4 to 5 over a 50 day recording period, indicating that the laboratory-designed neural probe is reliable and may be used successfully for long-term recordings. The somatosensory evoked potential (SSEP) obtained by thalamic stimulations and in vivo electrode-electrolyte interface impedance measurements was stable for 50 days and demonstrated that the neural probe is feasible for long-term stimulation. A strongly linear (positive correlation) relationship was observed among the simulated VTA, the absolute value of the SSEP during the 200 ms post-stimulus period (ΣSSEP) and c-Fos expression, indicating that the simulated VTA has perfect sensitivity to predict the evoked responses (c-Fos expression). This laboratory-designed neural probe and its FEM simulation represent a simple, functionally effective technique for studying DBS and neural recordings in animal models.

Transcranial imaging of functional cerebral hemodynamic changes in single blood vessels using in vivo photoacoustic microscopy.

Optical imaging of changes in total hemoglobin concentration (HbT), cerebral blood volume (CBV), and hemoglobin oxygen saturation (SO(2)) provides a means to investigate brain hemodynamic regulation. However, high-resolution transcranial imaging remains challenging. In this study, we applied a novel functional photoacoustic microscopy technique to probe the responses of single cortical vessels to left forepaw electrical stimulation in mice with intact skulls. Functional changes in HbT, CBV, and SO(2) in the superior sagittal sinus and different-sized arterioles from the anterior cerebral artery system were bilaterally imaged with unambiguous 36 × 65-µm(2) spatial resolution. In addition, an early decrease of SO(2) in single blood vessels during activation (i.e., 'the initial dip') was observed. Our results indicate that the initial dip occurred specifically in small arterioles of activated regions but not in large veins. This technique complements other existing imaging approaches for the investigation of the hemodynamic responses in single cerebral blood vessels.

Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors.

A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering.

Design, fabrication and experimental validation of a novel dry-contact sensor for measuring electroencephalography signals without skin preparation.

In the present study, novel dry-contact sensors for measuring electro-encephalography (EEG) signals without any skin preparation are designed, fabricated by an injection molding manufacturing process and experimentally validated. Conventional wet electrodes are commonly used to measure EEG signals; they provide excellent EEG signals subject to proper skin preparation and conductive gel application. However, a series of skin preparation procedures for applying the wet electrodes is always required and usually creates trouble for users. To overcome these drawbacks, novel dry-contact EEG sensors were proposed for potential operation in the presence or absence of hair and without any skin preparation or conductive gel usage. The dry EEG sensors were designed to contact the scalp surface with 17 spring contact probes. Each probe was designed to include a probe head, plunger, spring, and barrel. The 17 probes were inserted into a flexible substrate using a one-time forming process via an established injection molding procedure. With these 17 spring contact probes, the flexible substrate allows for high geometric conformity between the sensor and the irregular scalp surface to maintain low skin-sensor interface impedance. Additionally, the flexible substrate also initiates a sensor buffer effect, eliminating pain when force is applied. The proposed dry EEG sensor was reliable in measuring EEG signals without any skin preparation or conductive gel usage, as compared with the conventional wet electrodes.

Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis.

BACKGROUND: The computer-aided identification of specific gait patterns is an important issue in the assessment of Parkinson's disease (PD). In this study, a computer vision-based gait analysis approach is developed to assist the clinical assessments of PD with kernel-based principal component analysis (KPCA). METHOD: Twelve PD patients and twelve healthy adults with no neurological history or motor disorders within the past six months were recruited and separated according to their "Non-PD", "Drug-On", and "Drug-Off" states. The participants were asked to wear light-colored clothing and perform three walking trials through a corridor decorated with a navy curtain at their natural pace. The participants' gait performance during the steady-state walking period was captured by a digital camera for gait analysis. The collected walking image frames were then transformed into binary silhouettes for noise reduction and compression. Using the developed KPCA-based method, the features within the binary silhouettes can be extracted to quantitatively determine the gait cycle time, stride length, walking velocity, and cadence. RESULTS AND DISCUSSION: The KPCA-based method uses a feature-extraction approach, which was verified to be more effective than traditional image area and principal component analysis (PCA) approaches in classifying "Non-PD" controls and "Drug-Off/On" PD patients. Encouragingly, this method has a high accuracy rate, 80.51%, for recognizing different gaits. Quantitative gait parameters are obtained, and the power spectrums of the patients' gaits are analyzed. We show that that the slow and irregular actions of PD patients during walking tend to transfer some of the power from the main lobe frequency to a lower frequency band. Our results indicate the feasibility of using gait performance to evaluate the motor function of patients with PD. CONCLUSION: This KPCA-based method requires only a digital camera and a decorated corridor setup. The ease of use and installation of the current method provides clinicians and researchers a low cost solution to monitor the progression of and the treatment to PD. In summary, the proposed method provides an alternative to perform gait analysis for patients with PD.

Novel dry polymer foam electrodes for long-term EEG measurement.

A novel dry foam-based electrode for long-term EEG measurement was proposed in this study. In general, the conventional wet electrodes are most frequently used for EEG measurement. However, they require skin preparation and conduction gels to reduce the skin-electrode contact impedance. The aforementioned procedures when wet electrodes were used usually make trouble to users easily. In order to overcome the aforesaid issues, a novel dry foam electrode, fabricated by electrically conductive polymer foam covered by a conductive fabric, was proposed. By using conductive fabric, which provides partly polarizable electric characteristic, our dry foam electrode exhibits both polarization and conductivity, and can be used to measure biopotentials without skin preparation and conduction gel. In addition, the foam substrate of our dry electrode allows a high geometric conformity between the electrode and irregular scalp surface to maintain low skin-electrode interface impedance, even under motion. The experimental results presented that the dry foam electrode performs better for long-term EEG measurement, and is practicable for daily life applications.

A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese TTS system.

In this paper, a new technique for the Chinese text-to-speech (TTS) system is proposed. Our major effort focuses on the prosodic information generation. New methodologies for constructing fuzzy rules in a prosodic model simulating human's pronouncing rules are developed. The proposed Recurrent Fuzzy Neural Network (RFNN) is a multilayer recurrent neural network (RNN) which integrates a Self-cOnstructing Neural Fuzzy Inference Network (SONFIN) into a recurrent connectionist structure. The RFNN can be functionally divided into two parts. The first part adopts the SONFIN as a prosodic model to explore the relationship between high-level linguistic features and prosodic information based on fuzzy inference rules. As compared to conventional neural networks, the SONFIN can always construct itself with an economic network size in high learning speed. The second part employs a five-layer network to generate all prosodic parameters by directly using the prosodic fuzzy rules inferred from the first part as well as other important features of syllables. The TTS system combined with the proposed method can behave not only sandhi rules but also the other prosodic phenomena existing in the traditional TTS systems. Moreover, the proposed scheme can even find out some new rules about prosodic phrase structure. The performance of the proposed RFNN-based prosodic model is verified by imbedding it into a Chinese TTS system with a Chinese monosyllable database based on the time-domain pitch synchronous overlap add (TD-PSOLA) method. Our experimental results show that the proposed RFNN can generate proper prosodic parameters including pitch means, pitch shapes, maximum energy levels, syllable duration, and pause duration. Some synthetic sounds are online available for demonstration.

Genetic algorithm-based neural fuzzy decision tree for mixed scheduling in ATM networks.

Future broadband integrated services networks based on asynchronous transfer mode (ATM) technology are expected to support multiple types of multimedia information with diverse statistical characteristics and quality of service (QoS) requirements. To meet these requirements, efficient scheduling methods are important for traffic control in ATM networks. Among general scheduling schemes, the rate monotonic algorithm is simple enough to be used in high-speed networks, but does not attain the high system utilization of the deadline driven algorithm. However, the deadline driven scheme is computationally complex and hard to implement in hardware. The mixed scheduling algorithm is a combination of the rate monotonic algorithm and the deadline driven algorithm; thus it can provide most of the benefits of these two algorithms. In this paper, we use the mixed scheduling algorithm to achieve high system utilization under the hardware constraint. Because there is no analytic method for schedulability testing of mixed scheduling, we propose a genetic algorithm-based neural fuzzy decision tree (GANFDT) to realize it in a real-time environment. The GANFDT combines a GA and a neural fuzzy network into a binary classification tree. This approach also exploits the power of the classification tree. Simulation results show that the GANFDT provides an efficient way of carrying out mixed scheduling in ATM networks.