Multisensory Stimulation and EEG Recording Below the Hair-Line: A New Paradigm on Brain Computer Interfaces.

To test the feasibility of implementing multisensory (auditory and visual) stimulation in combination with electrodes placed on non-hair positions to design more efficient and comfortable Brain-computer interfaces (BCI). Fifteen volunteers participated in the experiments. They were stimulated by visual, auditory and multisensory stimuli set at 37, 38, 39 and 40Hz and at different phases (0°, 90°, 180° and 270°). The electroencephalogram (EEG) was measured from Oz, T7, T8, Tp9 and Tp10 positions. To evaluate the amplitude of the visual and auditory evoked potentials, the signal-to-noise ratio (SNR) was used and the accuracy of detection was calculated using canonical correlation analysis. Additionally, the volunteers were asked about the discomfort of each kind of stimulus. The multisensory stimulation allows for attaining higher SNR on every electrode. Non-hair (Tp9 and Tp10) positions attained SNR and accuracy similar to the ones obtained from occipital positions on visual stimulation. No significant difference was found on the discomfort produced by each kind of stimulation. The results demonstrated that multisensory stimulation can help in obtaining high amplitude steady-state evoked responses with a similar discomfort level. Then, it is possible to design a more efficient and comfortable hybrid-BCI based on multisensory stimulation and electrodes on non-hair positions. The current article proposes a new paradigm for hybrid-BCI based on steady-state evoked potentials measured from the area behind-the-ears and elicited by multisensory stimulation, thus, allowing subjects to achieve similar performance to the one achieved by visual-occipital BCI, but measuring the EEG on a more comfortable electrode location.

Web-Based System for the Remote Monitoring and Management of Precision Irrigation: A Case Study in an Arid Region of Argentina.

This article presents a description of the design, development, and implementation of web-based software and dedicated hardware which allows for the remote monitoring and control of a drip irrigation system. The hardware consists of in-field stations which are strategically distributed in the field and equipped with different sensors and communication devices; a weather station and drip irrigation system complete the setup. The web-based software makes it possible to remotely access and process the information gathered by all the stations and the irrigation controller. The proposed system was implemented in a young olive orchard, located in the province of San Juan, an arid region of Argentina. The system was installed and evaluated during the seasons 2014⁻2015 and 2015⁻2016. Four regulated irrigation strategies were proposed in the olive orchard to test its behavior. In this pilot experiment, the precision irrigation system was a useful tool for precisely managing the irrigation process, applying only the required amount of water (precise irrigation). Regulated deficit irrigation experiments, on the other hand, have demonstrated the sensitivity of olives to water restriction. The precision irrigation system made it possible to control soil moisture levels, avoiding water stress in the control treatment.

Attention-level transitory response: a novel hybrid BCI approach.

OBJECTIVE: People with disabilities may control devices such as a computer or a wheelchair by means of a brain-computer interface (BCI). BCI based on steady-state visual evoked potentials (SSVEP) requires visual stimulation of the user. However, this SSVEP-based BCI suffers from the 'Midas touch effect', i.e., the BCI can detect an SSVEP even when the user is not gazing at the stimulus. Then, these incorrect detections deteriorate the performance of the system, especially in asynchronous BCI because ongoing EEG is classified. In this paper, a novel transitory response of the attention-level of the user is reported. It was used to develop a hybrid BCI (hBCI). APPROACH: Three methods are proposed to detect the attention-level of the user. They are based on the alpha rhythm and theta/beta rate. The proposed hBCI scheme is presented along with these methods. Hence, the hBCI sends a command only when the user is at a high-level of attention, or in other words, when the user is really focused on the task being performed. The hBCI was tested over two different EEG datasets. MAIN RESULTS: The performance of the hybrid approach is superior to the standard one. Improvements of 20% in accuracy and 10 bits min(-1) are reported. Moreover, the attention-level is extracted from the same EEG channels used in SSVEP detection and this way, no extra hardware is needed. SIGNIFICANCE: A transitory response of EEG signal is used to develop the attention-SSVEP hBCI which is capable of reducing the Midas touch effect.

Commanding a robotic wheelchair with a high-frequency steady-state visual evoked potential based brain-computer interface.

This work presents a brain-computer interface (BCI) used to operate a robotic wheelchair. The experiments were performed on 15 subjects (13 of them healthy). The BCI is based on steady-state visual-evoked potentials (SSVEP) and the stimuli flickering are performed at high frequency (37, 38, 39 and 40 Hz). This high frequency stimulation scheme can reduce or even eliminate visual fatigue, allowing the user to achieve a stable performance for long term BCI operation. The BCI system uses power-spectral density analysis associated to three bipolar electroencephalographic channels. As the results show, 2 subjects were reported as SSVEP-BCI illiterates (not able to use the BCI), and, consequently, 13 subjects (12 of them healthy) could navigate the wheelchair in a room with obstacles arranged in four distinct configurations. Volunteers expressed neither discomfort nor fatigue due to flickering stimulation. A transmission rate of up to 72.5 bits/min was obtained, with an average of 44.6 bits/min in four trials. These results show that people could effectively navigate a robotic wheelchair using a SSVEP-based BCI with high frequency flickering stimulation.

Robust human machine interface based on head movements applied to assistive robotics.

This paper presents an interface that uses two different sensing techniques and combines both results through a fusion process to obtain the minimum-variance estimator of the orientation of the user's head. Sensing techniques of the interface are based on an inertial sensor and artificial vision. The orientation of the user's head is used to steer the navigation of a robotic wheelchair. Also, a control algorithm for assistive technology system is presented. The system is evaluated by four individuals with severe motors disability and a quantitative index was developed, in order to objectively evaluate the performance. The results obtained are promising since most users could perform the proposed tasks with the robotic wheelchair.

Asynchronous BCI control using high-frequency SSVEP.

BACKGROUND: Steady-State Visual Evoked Potential (SSVEP) is a visual cortical response evoked by repetitive stimuli with a light source flickering at frequencies above 4 Hz and could be classified into three ranges: low (up to 12 Hz), medium (12-30) and high frequency (> 30 Hz). SSVEP-based Brain-Computer Interfaces (BCI) are principally focused on the low and medium range of frequencies whereas there are only a few projects in the high-frequency range. However, they only evaluate the performance of different methods to extract SSVEP. METHODS: This research proposed a high-frequency SSVEP-based asynchronous BCI in order to control the navigation of a mobile object on the screen through a scenario and to reach its final destination. This could help impaired people to navigate a robotic wheelchair. There were three different scenarios with different difficulty levels (easy, medium and difficult). The signal processing method is based on Fourier transform and three EEG measurement channels. RESULTS: The research obtained accuracies ranging in classification from 65% to 100% with Information Transfer Rate varying from 9.4 to 45 bits/min. CONCLUSIONS: Our proposed method allows all subjects participating in the study to control the mobile object and to reach a final target without prior training.

SSVEP-BCI implementation for 37-40 Hz frequency range.

This work presents a Brain-Computer Interface (BCI) based on Steady State Visual Evoked Potentials (SSVEP), using higher stimulus frequencies (>30 Hz). Using a statistical test and a decision tree, the real-time EEG registers of six volunteers are analyzed, with the classification result updated each second. The BCI developed does not need any kind of settings or adjustments, which makes it more general. Offline results are presented, which corresponds to a correct classification rate of up to 99% and a Information Transfer Rate (ITR) of up to 114.2 bits/min.

Ultra wide-band localization and SLAM: a comparative study for mobile robot navigation.

In this work, a comparative study between an Ultra Wide-Band (UWB) localization system and a Simultaneous Localization and Mapping (SLAM) algorithm is presented. Due to its high bandwidth and short pulses length, UWB potentially allows great accuracy in range measurements based on Time of Arrival (TOA) estimation. SLAM algorithms recursively estimates the map of an environment and the pose (position and orientation) of a mobile robot within that environment. The comparative study presented here involves the performance analysis of implementing in parallel an UWB localization based system and a SLAM algorithm on a mobile robot navigating within an environment. Real time results as well as error analysis are also shown in this work.

A comparison of monopolar and bipolar EEG recordings for SSVEP detection.

This paper presents a comparative study over the detection of Steady-State Visual Evoked Potential (SSVEP) with monopolar or bipolar electroencephalographic (EEG) recordings in a Brain-Computer Interface experiment. Five subjects participated in this study. They were stimulated with four flickering lights at 13, 14, 15 and 16 Hz and the EEG was measured simultaneously with two bipolar channels (O(1)-P(3) and O(2)-P(4)) and with six monopolar channels at O(1), O(2), P(3), P(4), T(5) and T(6) referenced to F(Z). The EEG was processed by means of spectral analysis and the estimation of power at each stimulation frequency and its harmonics. In average, the monopolar recordings present accuracy in classification of 74.5% against an 80.1% for bipolar recordings. It was found that bipolar recording are better than monopolar recordings for detection of SSVEP.

Electrocutaneous stimulation system for Braille reading.

This work is an assistive technology for people with visual disabilities and aims to facilitate access to written information in order to achieve better social inclusion and integration into work and educational activities. Two methods of electrical stimulation (by current and voltage) of the mechanoreceptors was tested to obtain tactile sensations on the fingertip. Current and voltage stimulation were tested in a Braille cell and line prototype, respectively. These prototypes are evaluated in 33 blind and visually impaired subjects. The result of experimentation with both methods showed that electrical stimulation causes sensations of touch defined in the fingertip. Better results in the Braille characters reading were obtained with current stimulation (85% accuracy). However this form of stimulation causes uncomfortable sensations. The latter feeling was minimized with the method of voltage stimulation, but with low efficiency (50% accuracy) in terms of identification of the characters. We concluded that electrical stimulation is a promising method for the development of a simple and unexpensive Braille reading system for blind people. We observed that voltage stimulation is preferred by the users. However, more experimental tests must be carry out in order to find the optimum values of the stimulus parameters and increase the accuracy the Braille characters reading.

Vision based interface: an alternative tool for children with cerebral palsy.

In this paper a new vision based interface (VBI) for children with cerebral palsy is presented. The VBI is implemented for the interaction between children and computer. The VBI detects and tracks the movement of the hand, foot or head of the user. These movements are translated into movements of the cursor on the screen of the computer. The evaluation of system user-VBI is based on HAAT model. The experimental results show four vase studies of children, when they carried out different tasks with the computer.

Sleep apnea detection based on spectral analysis of three ECG - derived respiratory signals.

An apnea detection method based on spectral analysis was used to assess the performance of three ECG derived respiratory (EDR) signals. They were obtained on R wave area (EDR1), heart rate variability (EDR2) and R peak amplitude (EDR3) of ECG record in 8 patients with sleep apnea syndrome. The mean, central, peak and first quartile frequencies were computed from the spectrum every 1 min for each EDR. For each frequency parameter a threshold-based decision was carried out on every 1 min segment of the three EDR, classifying it as 'apnea' when its frequency value was below a determined threshold or as 'not apnea' in other cases. Results indicated that EDR1, based on R wave area has better performance in detecting apnea episodes with values of specificity (Sp) and sensitivity (Se) near 90%; EDR2 showed similar Sp but lower Se (78%); whereas EDR3 based on R peak amplitude did not detect appropriately the apneas episodes reaching Sp and Se values near 60%.

Application of the empirical mode decomposition to the extraction of features from EEG signals for mental task classification.

In this work, it is proposed a technique for the feature extraction of electroencephalographic (EEG) signals for classification of mental tasks which is an important part in the development of Brain Computer Interfaces (BCI). The Empirical Mode Decomposition (EMD) is a method capable to process nonstationary and nonlinear signals as the EEG. This technique was applied in EEG signals of 7 subjects performing 5 mental tasks. For each mode obtained from the EMD and each EEG channel were computed six features: Root Mean Square (RMS), Variance, Shannon Entropy, Lempel-Ziv Complexity Value, and Central and Maximum Frequencies, obtaining a feature vector of 180 components. The Wilks' lambda parameter was applied for the selection of the most important variables reducing the dimensionality of the feature vector. The classification of mental tasks was performed using Linear Discriminate Analysis (LD) and Neural Networks (NN). With this method, the average classification over all subjects in database was 91+/-5% and 87+/-5% using LD and NN, respectively. It was concluded that the EMD allows getting better performances in the classification of mental tasks than the obtained with other traditional methods, like spectral analysis.

A comparative study of the performance of different spectral estimation methods for classification of mental tasks.

In this paper we compare three different spectral estimation techniques for the classification of mental tasks. These techniques are the standard periodogram, the Welch periodogram and the Burg method, applied to electroencephalographic (EEG) signals. For each one of these methods we compute two parameters: the mean power and the root mean square (RMS), in various frequency bands. The classification of the mental tasks was conducted with a linear discriminate analysis. The Welch periodogram and the Burg method performed better than the standard periodogram. The use of the RMS allows better classification accuracy than the obtained with the power of EEG signals.