Gabriel Pires

A Wheelchair Steered through Voice Commands and Assisted by a Reactive Fuzzy-Logic Controller

This paper describes new results with a Reactive Shared-Control system that enables a semi-autonomous navigation of a wheelchair in unknown and dynamic environments. The purpose of the reactive shared controller is to assist wheelchair users providing an easier and safer navigation. It is designed as a fuzzy-logic controller and follows a behaviour-based architecture. The implemented behaviours are three: intelligent obstacle avoidance, collision detection and contour following. Intelligent obstacle avoidance blends user commands, from voice or joystick, with an obstacle avoidance behaviour. Therefore, the user and the vehicle share the control of the wheelchair. The reactive shared control was tested on the RobChair powered wheelchair prototype [6] equipped with a set of ranging sensors. Experimental results are presented demonstrating the effectiveness of the controller.

Statistical spatial filtering for a P300-based BCI: Tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis

The effective use of brain-computer interfaces (BCIs) in real-world environments depends on a satisfactory throughput. In a P300-based BCI, this can be attained by reducing the number of trials needed to detect the P300 signal. However, this task is hampered by the very low signal-to-noise-ratio (SNR) of P300 event related potentials. This paper proposes an efficient methodology that achieves high classification accuracy and high transfer rates for both disabled and able-bodied subjects in a standard P300-based speller system. The system was tested by three subjects with cerebral palsy (CP), two subjects with amyotrophic lateral sclerosis (ALS), and nineteen able-bodied subjects. The paper proposes the application of three statistical spatial filters. The first is a beamformer that maximizes the ratio of signal power and noise power (Max-SNR). The second is a beamformer based on the Fisher criterion (FC). The third approach cascades the FC beamformer with the Max-SNR beamformer satisfying simultaneously sub-optimally both criteria (C-FMS). The calibration process of the BCI system takes about 5 min to collect data and a couple of minutes to obtain spatial filters and classification models. Online results showed that subjects with disabilities have achieved, on average, an accuracy and transfer rate only slightly lower than able-bodied subjects. Taking 23 of the 24 participants, the averaged results achieved a transfer rate of 4.33 symbols per minute with a 91.80% accuracy, corresponding to a bandwidth of 19.18 bits per minute. This study shows the feasibility of the proposed methodology and that effective communication rates are achievable.

Visual P300-based BCI to steer a wheelchair: A Bayesian approach

This paper presents a new P300 paradigm for brain computer interface. Visual stimuli consisting of 8 arrows randomly intensified are used for direction target selection for wheelchair steering. The classification is based on a Bayesian approach that uses prior statistical knowledge of target and non-target components. Recorded brain activity from several channels is combined with a Bayesian sensor fusion and then events are grouped to improve event detection. The system has an adaptive performance that adapts to user and P300 pattern quality. The classification algorithms were obtained offline from training and then validated offline and online. The system achieved a transfer rate of 7 commands/min with 95% false positive classification accuracy.

Comparison of a row-column speller vs. a novel lateral single-character speller: Assessment of BCI for severe motor disabled patients

OBJECTIVE Non-invasive brain-computer interface (BCI) based on electroencephalography (EEG) offers a new communication channel for people suffering from severe motor disorders. This paper presents a novel P300-based speller called lateral single-character (LSC). The LSC performance is compared to that of the standard row-column (RC) speller. METHODS We developed LSC, a single-character paradigm comprising all letters of the alphabet following an event strategy that significantly reduces the time for symbol selection, and explores the intrinsic hemispheric asymmetries in visual perception to improve the performance of the BCI. RC and LSC paradigms were tested by 10 able-bodied participants, seven participants with amyotrophic lateral sclerosis (ALS), five participants with cerebral palsy (CP), one participant with Duchenne muscular dystrophy (DMD), and one participant with spinal cord injury (SCI). RESULTS The averaged results, taking into account all participants who were able to control the BCI online, were significantly higher for LSC, 26.11 bit/min and 89.90% accuracy, than for RC, 21.91 bit/min and 88.36% accuracy. The two paradigms produced different waveforms and the signal-to-noise ratio was significantly higher for LSC. Finally, the novel LSC also showed new discriminative features. CONCLUSIONS The results suggest that LSC is an effective alternative to RC, and that LSC still has a margin for potential improvement in bit rate and accuracy. SIGNIFICANCE The high bit rates and accuracy of LSC are a step forward for the effective use of BCI in clinical applications.

Playing Tetris with non-invasive BCI

This paper presents a non-invasive Brain Computer Interface (BCI) game that is inspired on the Tetris game. The BCI-Tetris is presented in three different versions. Two versions based on the P300 event related potential (ERP), and one version that combines the P300 ERP with the control of sensorimotor rhythms. The BCI-Tetris is being developed to be tested in pilot experiments with children with attention-deficit and hyperactivity disorder (ADHD). The results reported in this study with able-bodied participants show that the BCI-Tetris can be effectively controlled.

Wheelchair navigation assisted by Human-Machine shared-control and a P300-based Brain Computer Interface

This paper presents a new shared-control approach for assistive mobile robots, using Brain Computer Interface (BCI) as the Human-Machine Interface (HMI). A P300-based paradigm that allows the selection of brain-actuated commands to steer a Robotic Wheelchair (RW), is proposed. At least one specific motor skill, such as the control of arms, legs, head or voice, is required to operate a conventional HMI. Due to this reason, they are not suited for people suffering from severe motor disorders. BCI may open a new communication channel to these users, since it does not require any muscular activity. The number of decoded symbols per minute (SPM) in a BCI is still very low, which means that users can only provide sparse, and discrete commands. The RW must rely on the navigation system to validate user commands effectively. A two-layer shared-control approach is proposed. The first, a virtual-constraint layer, is responsible for enabling/disabling the user commands, based on certain context restrictions. The second layer is an user-intent matching responsible for determining the suitable steering command, that better fits the user command, taking the user competence on steering the wheelchair into account. Experimental results using Robchair, the RW platform developed at ISR-UC [1], [2] are presented, showing the effectiveness of the proposed methodologies.

Efficient feature selection for sleep staging based on maximal overlap discrete wavelet transform and SVM

In this paper, a novel algorithm is proposed with application in sleep/awake detection and in multiclass sleep stage classification (awake, non rapid eye movement (NREM) sleep and REM sleep). In turn, NREM is further divided into three stages denoted here by S1, S2, and S3. Six electroencephalographic (EEG) and two electro-oculographic (EOG) channels were used in this study. The maximum overlap discrete wavelet transform (MODWT) with the multi-resolution Analysis is applied to extract relevant features from EEG and EOG signals. The extracted feature set is transformed and normalized to reduce the effect of extreme values of features. A set of significant features are selected by mRMR which is a powerful feature selection method. Finally the selected feature set is classified using support vector machines (SVMs). The system achieved 95.0% of average accuracy for sleep/awake detection. As concerns the multiclass case, the average accuracy of sleep stages classification was 93.0%.

Autonomous wheelchair for disabled people

This paper describes the RobChair assistive navigation system. The RobChair project was conceived with the aim to assist disabled people in the difficult task of manoeuvring a powered wheelchair. This paper describes the overall hardware and software architecture including the communication system, a friendly graphical user interface which also works as a simulator and introduces a natural human-machine interface. The systems architecture follows a behaviour-based control architecture.

GIBS block speller: Toward a gaze-independent P300-based BCI

Brain-computer interface (BCI) opens a new communication channel for individuals with severe motor disorders. In P300-based BCIs, gazing the target event plays an important role in the BCI performance. Individuals who have their eye movements affected may lose the ability to gaze targets that are in the visual periphery. This paper presents a novel P300-based paradigm called gaze independent block speller (GIBS), and compares its performance with that of the standard row-column (RC) speller. GIBS paradigm requires extra selections of blocks of letters. The online experiments made with able-bodied participants show that the users can effectively control GIBS without moving the eyes (covert attention), while this task is not possible with RC speller. Furthermore, with overt attention, the results show that the improved classification accuracy of GIBS over RC speller compensates the extra selections, thereby achieving similar practical bit rates.

Evaluation of Brain-computer Interfaces in Accessing Computer and other Devices by People with Severe Motor Impairments

A brain-computer interface (BCI) translates brain signals into commands that can be used to control computer applications or external devices. BCI provides a non-muscular communication channel and therefore it assumes a crucial importance for individuals with motor functions severely affected. The evaluation of BCI by individuals with severe disabilities is of utmost importance to understand the BCI feasibility as an assistive technology. This paper summarizes some of the results achieved in our research lab, with different BCIs tested by individuals with severe motor disabilities, focusing on some practical aspects of BCI evaluation, and on the target population.