Machine-Learning-Based Coadaptive Calibration for Brain-Computer Interfaces

Machine Learning Department, Berlin Institute of Technology, Berlin 10587, Germany .
Neural Computation (Impact Factor: 2.21). 12/2010; 23(3). DOI: 10.1162/NECO_a_00089
Source: PubMed


Brain-computer interfaces (BCIs) allow users to control a computer application by brain activity as acquired (e.g., by EEG). In our classic machine learning approach to BCIs, the participants undertake a calibration measurement without feedback to acquire data to train the BCI system. After the training, the user can control a BCI and improve the operation through some type of feedback. However, not all BCI users are able to perform sufficiently well during feedback operation. In fact, a nonnegligible portion of participants (estimated 15%–30%) cannot control the system (a BCI illiteracy problem, generic to all motor-imagery-based BCIs). We hypothesize that one main difficulty for a BCI user is the transition from offline calibration to online feedback. We investigate adaptive machine learning methods to eliminate offline calibration and analyze the performance of 11 volunteers in a BCI based on the modulation of sensorimotor rhythms. We present an adaptation scheme that individually guides the user. It starts with a subject-independent classifier that evolves to a subject-optimized state-of-the-art classifier within one session while the user interacts continuously. These initial runs use supervised techniques for robust coadaptive learning of user and machine. Subsequent runs use unsupervised adaptation to track the features’ drift during the session and provide an unbiased measure of BCI performance. Using this approach, without any offline calibration, six users, including one novice, obtained good performance after 3 to 6 minutes of adaptation. More important, this novel guided learning also allows participants with BCI illiteracy to gain significant control with the BCI in less than 60 minutes. In addition, one volunteer without sensorimotor idle rhythm peak at the beginning of the BCI experiment developed it during the course of the session and used voluntary modulation of its amplitude to control the feedback application.

39 Reads
  • Source
    • "In other words, stronger ERD/ ERS indicates how well a subject performs MI task and subsequently controls an EEG-based MI-BCI. However, there is a large variation in MI-BCI performance of the subjects [5], and the reason why some subjects cannot use MI-BCI to achieve even moderate performance is not well studied. BCI deficiency, the subjects' inability to modulate their brain rhythms, is one of the main reasons of poor MI-BCI performance [6], which limits the applicability of BCI technology. "
    [Show abstract] [Hide abstract]
    ABSTRACT: The subjects' performance in using a brain-computer interface (BCI) system controlled by motor imagery (MI) varies considerably. Poor subjects' performance, known as BCI deficiency, can be due to the subjects' inability to modulate their sensorimotor rhythms (SMRs). In this work, we investigated the feasibility of improving the BCI performance through neurofeedback (NF) training of the resting state alpha rhythm (8-13 Hz). Thirteen healthy subjects were recruited and randomly assigned to the experimental or the control group. The experimental group participated in a MI-BCI session, followed by 12 NF sessions, and a final MI-BCI sessions. The control group performed a MI-BCI session followed by a final MI-BCI session. The results showed that the performances of the experimental group after 12 sessions of NF significantly improved upon the initial MI-BCI performance (p=0.02) but not the control group (p=0.14). Moreover, the resting state alpha of the experimental group significantly improved after 12 sessions of NF (p=0.04). In conclusion, the proposed approach is promising to address BCI deficiency.
  • Source
    • "While Dura-Bernal and colleagues [10] have argued that such a biomimetic neuronal model could be used for Brain- Computer Interfaces (BCIs) to translate brain activity for control of a robotic limb, we want to use this model in the opposite direction. Adaptive decoding methods, that adapt to the users brain signals, have recently been shown to improve BCI performance [11], [12], [13]. The currently fastest noninvasive BCI uses unsupervised learning and error-related potentials [14] for adaptation to reach information transfer rates of more than 140 bit/min which allows to write more than 20 error-free characters per minute [15], showing that adaptation is a key aspect in BCI. "
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we present a spiking neuronal model that learns to perform a motor control task. Since the long-term goal of this project is the application of such a neuronal model to study the mutual adaptation between a Brain-Computer Interface (BCI) and its user, neurobiological plausibility of the model is a key aspect. Therefore, the model was trained using reinforcement learning similar to that of the dopamine system, in which a global reward and punishment signal controlled spike-timing dependent plasticity (STDP). Based on this method, the majority of the randomly generated models were able to learn the motor control task. Although the models were only trained on two targets, they were able to reach arbitrary targets after learning. By introducing structural synaptic plasticity (SSP), which dynamically restructures the connections between neurons, the number of models that successfully learned the task could be significantly improved.
    The 2015 International Joint Conference on Neural Networks; 07/2015
  • Source
    • "There have been a number of recent efforts to learn improved adaptive decoders specifically tailored for the closed loop setting [9] [10], including an approach relying on stochastic optimal control theory [11]. In other contexts, emphasis has been placed on training users to improve closed-loop control [12]. Some efforts towards modeling the co-adaptation process have sought to model properties of different decoders when used in closed-loop [13] [14] [15], with emphasis on ensuring the stability of the decoder and tuning the adaptation rate. "
    [Show abstract] [Hide abstract]
    ABSTRACT: In a closed-loop brain-computer interface (BCI), adaptive decoders are used to learn parameters suited to decoding the user’s neural response. Feedback to the user provides information which permits the neural tuning to also adapt. We present an approach to model this process of co-adaptation between the encoding model of the neural signal and the decoding algorithm as a multi-agent formulation of the linear quadratic Gaussian (LQG) control problem. In simulation we characterize how decoding performance improves as the neural encoding and adaptive decoder optimize, qualitatively resembling experimentally demonstrated closed-loop improvement. We then propose a novel, modified decoder update rule which is aware of the fact that the encoder is also changing and show it can improve simulated co-adaptation dynamics. Our modeling approach offers promise for gaining insights into co-adaptation as well as improving user learning of BCI control in practical settings.
    Neural Information Processing Systems (NIPS); 12/2013
Show more