Figure 1. A conceptual diagram of the potential role of BCI driven telepresence robotics in the restoration of autonomy to a paralyzed individual. The bioelectric signal generated from motor imaginations of the hands is represented in the background of the figure. The signal is acquired through the amplifiers in the subject's workstation where it is then digitized and passed to the computer system. Filtering and further processing of the signal results in a conversion to a control signal that can determine the movement of the quadcopter. This signal is sent regularly via WiFi to the quadcopter to update its movement, while the quadcopter simultaneously acquires video and sends it back to the computer workstation. The subject adjusts control and adapts to the control parameters of the system based on the visual feedback of the quadcopter's video on the computer screen. Restoration of autonomy and the ability to freely explore the world are the driving factors for the development of the system and can be expanded to control of any number of robotic telepresence or replacement systems.

In the present study, we investigate the ability to control the flight of a flying object in 3D physical space using SMRs derived from noninvasive EEG in a group of human subjects. Subjects received feedback while flying from a real time video stream that was captured from an onboard camera. The application of telepresence in BCI technology, along with the employment of a continuous, fast and accurate control system are crucial elements in the implementation of a BCI system that has the ability to depart from the immediate surroundings of the user and enter locations where these systems are needed. Figure 1 shows a conceptual diagram of the present study and the potential role of BCI driven telepresence robotics in the restoration of autonomy to a paralyzed individual. The bioelectric signal generated from motor imagination of the hands is represented in the background of the figure. The control signal decoded from the scalp EEG is sent regularly via WiFi to the quadcopter to update its movement, while the quadcopter simultaneously acquires video and sends it back to the computer workstation. While a quadcopter was chosen due to its low cost and robust capacity for programmable actuation, the reader may envision any combination of remote mobile devices capable of meaningful interaction with the 3D world. Restoration of autonomy and the ability to freely explore the world through these means are the driving factors for the present investigation.

In previous studies, we have demonstrated the ability of users to control the flight of a virtual helicopter with 2D control (Royer et al 2010 ) and 3D control (Doud et al 2011 ) by leveraging a motor imagery paradigm with intelligent control strategies. In these studies, in which subjects imagined moving parts of their bodies in a real-time setup, SMRs were extracted to control the movement of a virtual helicopter.

Patients suffering from amyotrophic lateral sclerosis (ALS) have often been identified as a population who may benefit from the use of a BCI (Bai et al 2010 ). The components of a satisfactory system were identified in a recent ALS patient survey as highly accurate command generation, a high speed of control, and low incidence of unintentional system suspension, i.e. continuity of control (Huggins et al 2011 ). While these needs were reported from a patient population with ALS, similar if not identical needs are likely identifiable in a wide variety of other neurodegenerative disorders and the identification of these needs is a crucial component of future investigation. However, the efficacy of noninvasive SMR-based BCIs is supported by research indicating that the ability to generate SMRs remains present in users with other neurodegenerative disorders such as muscular dystrophy and spinal muscular atrophy (Cincotti et al 2008 ). It is crucial that researchers developing BCIs keep these identified patient-relevant parameters in mind when developing new systems. One limitation of many BCI systems has been the need for a fixed schedule of events. For patients suffering from various neuromuscular disorders, a fixed schedule of command production would limit autonomy (Scherer et al 2008 ). Our BCI does not require a fixed schedule of commands, but this makes using some standard BCI metrics impossible (Kronegg et al 2005 , Yuan et al 2013 ). In order to maintain consistency with the common metrics reported, including the metric of information transfer rate (ITR) (McFarland et al 2003 ), we used a modified ITR to fit our experimental protocol. In addition, we reported ITR values for the two-dimensional (2D) cursor task that were calculated by BCI2000 using standard techniques. Using a modified ITR calculation has been recently suggested in the literature (Yuan et al 2013 ). Other metrics include the success and failure rates for the acquisition of presented targets. Protocols developed for the assessment of BCIs that allow for asynchronous interaction and exploration of the subject's surroundings will be best equipped to aid in the development of systems with a human focus. Ultimately the development of these systems may some day help to restore that which has been undermined by disease.

The development of BCIs is aimed at providing users with the ability to communicate with the external world through the modulation of thought. Such a task is achieved through a closed loop of sensing, processing and actuation. Bioelectric signals are sensed and digitized before being passed to a computer system. The computer then interprets fluctuations in the signals through an understanding of the underlying neurophysiology, in order to discern user intent from the changing signal. The final step is the actuation of this intent, in which it is translated into specific commands for a computer or robotic system to execute. The user can then receive feedback in order to adjust his or her thoughts, and then generates new and adapted signals for the BCI system to interpret.

Brain–computer interfaces (BCIs) are aimed at restoring crucial functions to people that are severely disabled by a wide variety of neuromuscular disorders, and at enhancing functions in healthy individuals (Wolpaw et al 2002 , Vallabhaneni et al 2005 , He et al 2013 ). Significant advances have been made in the development of BCIs where intracranial electrophysiological signals are recorded and interpreted to decode the intent of subjects and control external devices (Georgopoulos et al 1982 , Taylor et al 2002 , Musallam et al 2004 , Hochberg et al 2006 , Santhanam et al 2006 , Velliste et al 2008 , Hochberg et al 2012 ). Noninvasive BCIs have also long been pursued from scalp recorded noninvasive electroencephalograms (EEGs). Among such noninvasive BCIs, sensorimotor rhythm (SMR)-based BCIs have been developed using a motor imagery paradigm (Pfurtscheller et al 1993, Wolpaw et al 1998 , Wolpaw and McFarland 2004 , Wang and He 2004 , Wang et al 2004 , Qin et al 2004 , Kamousi et al 2005 , Qin and He 2005 , Galán et al 2008 , Yuan et al 2008 , 2010a , 2010b , McFarland et al 2010 , Doud et al 2012 ).

Study overview

The study consisted of training and calibration phases, an experimental task phase, and an experimental control phase. In addition, the intrinsic ease of the experimental task was quantified to serve as a baseline comparison to better characterize the achievements of the subjects. Prior to participation in the experimental phase of the study, each subject had received exposure to 1D and 2D cursor movement tasks using motor imagery. One of the subjects had also received training in virtual helicopter control experiments (Royer et al 2010, Doud et al 2011). Regardless of training background, subjects were asked to demonstrate proficiency in 1D and 2D cursor control prior to progression to the AR Drone quadcopter training. Subjects who demonstrated the ability to correctly select 70% or more of valid targets in each of four consecutive 2D cursor trials, or who achieved an average of 70% or more of valid targets over ten consecutive 2D trials, were deemed proficiently skilled in BCI control for participation in the AR Drone quadcopter study. During the initial training period, a statistical optimization of the control signal was performed using the Offline Analysis toolbox released with the BCI2000 development platform (Schalk et al 2004). This software allows researchers to identify the specific electrodes and frequencies that were most differentially active during the actuation of a given imagination pair. Spectrograms of the R2 value, a statistical measure of degree of correlation of temporal components of the EEG signal with different imagination state pairings, were created so that the electrode and frequency bin (3 Hz width) with the highest correlation value to a given imagination state could be used. In this way, the training period was crucial in determining an optimal, subject-specific control signal that prepared each subject for entry into the real-world task. Figures 2 and 3 show the statistical analysis involved in selecting a control signal for a representative subject. The Offline Analysis toolbox produces a feature map of the R2 value at each frequency and electrode, a topographical representation at a user-specified frequency, and a single-electrode representation of the R squared value that varies with frequency. By evaluating these three figures, a researcher may quickly identify a subject specific electrode-frequency configuration that will best serve the subject as a control signal for a motor imagery-based BCI. Electrode selection for each subject can be seen in table 3 in the supplementary materials (available at stacks.iop.org/JNE/10/046003/mmedia). Figure 2. (a), (b) A characteristic subject's R2 topography for the 12 Hz component of the right and left hand imaginations as compared to rest. A high R2 value indicates a region in the 12 Hz band that is activated differentially between the imagined motion and the rest state. Event related desynchronization in the 12 Hz frequency region that is located ipsilateral to the imagined hand is a well-characterized response to motor imagery. (c) The R2 topography of the 12 Hz component of the imagination of both hands as compared to rest. Download figure: Standard image High-resolution image Figure 3. Spectral power is shown in the control electrodes as a function of frequency. (a), (b) Comparisons of spectral power in right versus left hand imaginations. Note that spectral power is diminished in the electrode contralateral to the imagined hand and increased in the ipsilateral electrode. As a result of the differential synchronization-desynchronization of the electrodes in response to the unilateral motor task, subtracting the C3 electrode from the C4 electrode results in a more separable signal for left-right control. (c), (d) Comparisons of spectral power in response to both hands imagined versus rest show increased power in response to the rest state and desynchronization in response to the imagination of both hands. Both electrodes show desynchronized activity in response to the imaginative state, allowing them to be summed to produce a separable signal for up-down control. (e), (f) These spectral plots show left versus rest for the same trials as those constituting the topographies in figure 2. Download figure: Standard image High-resolution image

Experimental subjects

Five human subjects, aged 21–28 (three female and two male), were included in the study. Each subject provided written consent in order to participate in a protocol approved by the Institutional Review Board of the University of Minnesota. Four of the subjects had not been exposed to BCI before this study, while the fifth subject had been trained and participated in the previous virtual helicopter study detailed in Doud et al (2011).

Subject training

The initial training phase of the study was aimed at achieving competence in 2D control in the standard BCI 2000 cursor task in addition to a virtual helicopter simulator program developed and described in Royer et al (2010). The reductionist control strategy that was employed in the Royer protocol proved to be a robust control system that was relatively quick and easy to learn. Thus, this protocol was chosen for the transition from virtual to real-world control. Subjects were introduced to and trained in the 1D cursor task (Yuan et al 2008, Royer and He 2009) until they achieved a score of 80% or above in four consecutive 3 min trials, or until they achieved a score of 80% or more when performance was averaged across ten or more consecutive trials. In the 1D cursor task, a target appeared on either the left or right side of the screen and was illuminated with a yellow colour. The subject was instructed to perform motor-imagery of left or right hand movement to guide a cursor to the illuminated target, while avoiding an invisible target, which was located on the side of the screen opposite the desired target. Subjects were given three experimental sessions, each consisting of at least nine experimental trials (3 min each) to complete this first phase. A second dimension was then trained independently of the first. Subjects were presented with a second cursor task in which targets appeared at either the top or bottom of the screen. In this task, subjects were instructed to move the cursor up by imagining squeezing or curling both hands and to move the cursor down through the use of a volitional rest. The rest imagination often consisted of subjects focusing on a non-muscular part of the body and relaxing. In this task, addition of the normalized amplitude of the desired frequency components produced the up-down control signal, whereas the left-right control signal was generated from the subtraction of these same frequency components. This allowed for independent control because simultaneous imagination of the hands cancels out the subtracted left-right component, while a lateralized left or right imagination in isolation causes a difference in signal sign that cancels the additive up-down component. Rules for progressing from this phase were the same as in the previous phase, with a limit of three experimental sessions for completion. The third phase paired the control signals from the first two phases together in a 2D cursor task with an array of four targets in positions on the top, bottom, left, and right sides of the monitor. Subjects progressed to the next phase of training when they could correctly select 70% or more of valid targets in four consecutive 2D cursor trials or an average of 70% or more of valid targets over ten consecutive 2D trials. After meeting these criteria, subjects progressed to controlling a virtual helicopter (Royer et al 2010). The kinetics of this virtual helicopter operated according to a linear tie of velocity to the normalized amplitude of EEG signal components. After up to three sessions (for a total average of about 1 h of flight time) of training to gain familiarity with this virtual helicopter control protocol, subjects were trained with an enhanced virtual simulation of the AR Drone quadcopter. This improved simulator included an updated virtual model of the AR Drone quadcopter, similar in appearance to the physical quadcopter subjects would eventually be flying in 3D real space. The new simulation program also had an improved control algorithm. It used the same Blender platform physics simulator that was used in Doud et al (2011), but the new algorithm employed force actuation of the drone movement to better approximate the behaviour of the actual AR Drone in a real physical environment. This new algorithm linked subjects' BCI control signal to the acceleration that was applied to the quadcopter rather than to the velocity of the quadcopter. By actuating the virtual quadcopter through force, we excluded from the system some unrealistic movements that could arise from directly updating the velocity of the model in coordination with the control signal. While this force actuation presented a novel challenge to the subjects, it was an important step in exposing the subjects to the real-world forces that must be managed in order to successfully control the AR Drone. More information on the simulator and its control setup can be found in the supplementary materials (available at stacks.iop.org/JNE/10/046003/mmedia). Subject interaction with this simulation system was qualitatively evaluated with the major goal being to familiarize the subject with the control setup, without the pressure of steering an actual robotic drone. After one to three sessions (consisting of at least nine 3 min experimental trials each) of training with the virtual AR Drone, subjects started to train with the real AR Drone. The four subjects that were naïve to BCI prior to this study spent an average of 5 h and 20 min of virtual training over an average period of three months before starting to train with the real AR Drone. This time includes the 1D left versus right, 1D up versus down, and 2D cursor tasks, in addition to the virtual helicopter simulation. The order of progression of training can be seen in the flowchart in figure 4. Figure 4. An outline of the subjects' progression through the training sequence that was developed to promote a robust 2D motor imagery-based control. During the first three stages of cursor task training, the research team performed an optimization of electrode position and frequency components that contributed to each subject's control signal. The virtual helicopter task was used to familiarize the subjects with the control system for the AR Drone. The final stage of training was to navigate the AR Drone robotic quadcopter in a real-world environment. Download figure: Standard image High-resolution image Upon completion of the virtual tasks, subjects were given an opportunity to calibrate their control signals and familiarize themselves with the robotic system by way of a novice-pilot training task. In both the experimental task and the novice-pilot training task, subjects were asked to pilot the AR Drone quadcopter safely through suspended-foam rings. However, in the novice-pilot training task, the rings were spaced further apart and subjects were instructed to fly back and forth such that no ring was acquired more than once in a row. The design of the novice-pilot training task encouraged the user to explore the control space, and gave the subject an opportunity to suggest refinements to the sensitivity of each of the BCI controls. The experimental group spent an average of 84 min over three weeks on the novice pilot training task. When the subject felt comfortable with the calibration of the control signal in the novice-pilot training task, they were moved to the experimental protocol where speed, accuracy and continuity of control were assessed with the experimental task described below.

Data collection and processing

Subjects sat in a comfortable chair, facing a computer monitor in a standard college gymnasium. The experimental set-up was such that a solid wall obscured the subject's view of the quadcopter. A 64-channel EEG cap was securely fitted to the head of each subject according to the international 10–20 system. EEG signals were sampled at 1000 Hz and filtered from dc 200 Hz by a Neuroscan Synamps 2 amplifier (Neuroscan Lab, VA) before they were imported into BCI2000 (Schalk et al 2004) with no spatial filtering. Details of electrode and frequency selection can be seen in the supplementary materials (available at stacks.iop.org/JNE/10/046003/mmedia). The parameterization of BCI2000 during the AR Drone task was such that 4 min runs consisting of 6 s trials ran in the background of the drone control program and communicated the raw control signal via the UDP packet protocol. The trials were set to have no inter-trial interval or post feedback duration. During each BCI2000 trial, the standard cursor moved through a 2D control space for 6 s before being reset to the centre of the control space for the next trial. Since there was no inter-trial interval, this 6 s trial pacing was not perceptible by the subject, but was performed to ensure that the system was normalized regularly. Normalization to impose a zero mean and unit variance was performed online with a 30 s normalization buffer. The output signal during cursor movement for all target attempts entered the normalization buffer and was recalculated between trials. The control signal was extracted as the spectral amplitude of the chosen electrodes at the selected frequency components. This was accomplished using BCI2000's online Autoregressive Filter set with a model order of 16 operating on a time window of 160 ms for spectrum calculation. Effectively, this configuration resulted in continuous output of control signal, pauses for recalculation of normalization factors imperceptible to the subject. Subjects attended three days of the experimental protocol with each daily session consisting of 6–15 trials, lasting up to four minutes each. Subjects were visually monitored for inappropriate eye blinking or muscle activity during each session and were regularly reminded of the importance of minimizing movement. However, observed eye blinking and muscle movement were minimal during all of the experimental sessions. The time-varying spectral component amplitudes from the EEG at predetermined subject-specific electrode locations and frequencies were selected and then integrated online by BCI2000 to produce a control signal that was sent every 30 ms via a UDP port to a Java program that communicated wirelessly with the AR Drone.

Control environment and quadcopter

The quadcopter flight environment was set up in a standard college gymnasium. Figure 5 illustrates the experimental setup of the flight environment, and the quadcopter's starting position relative to the two ring-shaped targets. These targets were made of lightweight foam and were suspended so that the internal edge of the bottom of the ring was approximately 1 m above the ground. Each ring had an outer diameter of 2.7 m and an inner diameter of 2.29 m. Subjects were situated to be facing a solid wall on the side of the gymnasium to ensure safety and to obstruct the subject's view of the AR Drone quadcopter. Thus, subjects did not directly see the quadcopter, but watched a computer monitor with a first person view from a camera that was mounted on the hull of the quadcopter. Figure 5. The layout of the experimental set-up, as well as the dimensions of the quadcopter control space can be seen. The inner diameter of the ring target is 2.29 m. A small indentation in the control space is seen on the bottom wall where the subject was located during the experiment; thus the quadcopter was not allowed to enter this area. Download figure: Standard image High-resolution image The AR Drone 1.0 quadcopter (Parrot SA, Paris, France) in the described control space was chosen for the experiments because it provides strong onboard stabilization while allowing for a wide range of programmable speeds and a smooth range of motion. It is a relatively low-cost option for robust control in 3D space with extensive open source support. In addition, the AR Drone provided access to two onboard cameras, and the accelerometer, altitude, and battery data were all reported to the control software and recorded in real time.

Experimental paradigm

The experimental protocol consisted of three experimental sessions for each subject, with 6–15 trials per session and a maximum flight time of 4 min per trial. Each trial began with the AR Drone quadcopter 0.8 m off the ground. Imagining use of the right hand turned the quadcopter right, while imagination of the left hand turned it left. Imagining both hands together caused the helicopter to rise, while intentionally imagining nothing caused it to fall. A constant forward signal was sent to the quadcopter such that the linear velocity was measured to be 0.69 ± 0.02 m s−1 in the absence of rotational movement. Figure 6 displays the actuations of the quadcopter that correspond to the subjects' motor imaginations. Turning the quadcopter attenuated its forward velocity significantly. This is an example of a real-world control parameter that was absent in virtual simulations. An example of this attenuation can be seen in supplementary video 3 (available at stacks.iop.org/JNE/10/046003/mmedia). Subjects were allowed to pass through the rings in any order, on the condition that no event (i.e. ring collision or target acquisition) occurred within 5 s of the previous event. A demonstration of two short, yet separable, acquisitions can be seen in supplementary video 4 (available at stacks.iop.org/JNE/10/046003/mmedia). This time requirement prevented multiple successes from a single target attempt. Figure 6. Actuation of the drone is represented by the coloured arrows. The motor imaginations corresponding to each control are seen in the legend on the right. Separable control of each dimension was attained by imagining clenching of the left hand for left turns, right hand for right turns, both hands for an increase in altitude, and no hands for a decrease in altitude. The strength of each control could be independently controlled by weighting coefficients. Download figure: Standard image High-resolution image In comparison to the novice-pilot training task, the experimental protocol allowed subjects to expand their strategies, while still ensuring that no two events came from the same intentional act. In the experimental task, the rings were positioned such that each ring was 4.6 m from the take-off location of the drone. In order to maximize control, subjects were given a system of actuation that is commonly used for remote control vehicles. This was accomplished by using a nonlinear transformation of the EEG signal before the control signal was sent to the quadcopter. A graph of this transformation can be seen in figure 7. The signal used a threshold to remove minor signals that were not likely to have originated from intentional control. Beyond this threshold, the signal was transformed by a quadratic function so that the subjects were able to alter the strength of imagined actions to either achieve fine control for acquiring rings or to make large turns for general steering purposes and obstacle avoidance. Figure 7. Control of the AR Drone is shown in terms of the amount of yaw, forward pitch, and altitude acceleration of the drone with 1 being a maximum forward/right/upwards signal, and −1 being a maximum backwards/left/downwards actuation. The constant forward signal was set to correspond to 15% maximum forward tilt and indirectly to a percent of the drone's maximum forward speed. For the UD signal, maximum actuation would cause the helicopter to supply the maximum amount of lift or to slow its engines in the presence of a negative signal. For the LR signal, maximum actuation refers to the generation of maximum rotational force about the helicopters z-axis. On the x-axis, the BCI2000 generated control signal is the cumulative amplitude of the temporal component of interest for a given electrode. Near the mean signal of zero, a threshold was used to assign a value of zero to small, erroneous signals from randomly generated noise being sent to the drone in the absence of subject intention. The signals could be adjusted by weighting factors for each subject. The relevant equations are shown in the supplementary data (available in stacks.iop.org/JNE/10/046003/mmedia). This graph displays the weighted signal used by subject 1 in the second protocol. Download figure: Standard image High-resolution image If a subject successfully navigated the quadcopter to pass through a ring, a 'target hit' was awarded; however, if the subject only collided with the ring, a 'ring collision' was awarded. If the quadcopter collided with a ring, and then passed through that same ring within 5 s, a target hit but not a ring collision was awarded. Finally, if the quadcopter went outside of the boundaries of the control space, the trial was ended and the subject was assigned a 'wall collision'. Trials that lasted for less than four minutes ended because the quadcopter either prematurely exited the bounds of the control space (i.e. a wall collision) or a drone malfunction occurred; malfunctions consisted of occasional wireless network failures or mechanical errors that were outside of the users control. These occasional malfunctions due to mechanical errors or the loss of wireless signal caused a small subset of trials to end early. The data from these trials were analyzed prior to the malfunction, but no wall collision was assigned to the subject's performance log, as the error was not within their control.

Experimental control

A control was performed using an experimental protocol in which one naïve subject and two experimental subjects controlled the flight of the quadcopter using keyboard control instead of a BCI. In this protocol, the quadcopter was given the same constant forward velocity, as well as maximum altitude gains and turning speeds that were equivalent to the average of all trained subjects. This protocol allowed for a comparison between a standardized method of control and BCI control. The keyboard control had one advantage in that a 'total zero' signal could easily be maintained such that no left/right or up/down actuation occurred. This allowed for easier achievement of straight-line flight in the control experiments. Although it can be argued that a joystick or a remote control box would be a more nuanced method of controlling the quadcopter, this control paradigm employed the discrete keyboard actuation to ensure that the actuated control was the fastest possible within the constrained parameters. In the keyboard control task, subjects were given the same instructions with regards to the manner in which the rings were to be acquired. In addition, the intrinsic ease of the experimental task was assessed. It is common practice in the field of BCI to propose a new and novel task for a BCI to perform. When such a task has not yet been explored by other researchers, it can become difficult for readers to determine how much of the subject's success is attributable to the control system, and how much is attributable to the inherent characteristics of the task. In a task that reports that a subject acquires a target 100% of the time, it is important to also know the target density and how well the system performs when given random noise. If the system can still acquire 100% of the targets in the absence of user intent, the result becomes trivial. To evaluate the intrinsic ease of the presented experimental task, a naïve subject was shown a sham video feed of the helicopter moving in the control space. The subject was instructed to simply observe the screen while sitting quietly and still. In all other ways the system was set up in a manner that is identical to the experimental protocol. The subject wore an EEG cap and signals were acquired that controlled the action of an AR Drone quadcopter in the control space. The performance of the AR Drone in the absence of user intent was measured. This setup was preferred to ensure that realistic biological signals were used as the input of the system; thus, poor performance could not be attributed to a choice of random noise of inappropriate magnitude or characteristics.

Performance analysis