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Brain-computer interfaces (BCIs) provide a communication interface between the brain and external devices and have the potential to restore communication and control in patients with neurological injury or disease. For the invasive BCIs, most studies recruited participants from hospitals requiring invasive device implantation. Three widely used clinical invasive devices that have the potential for BCIs applications include surface electrodes used in electrocorticography (ECoG) and depth electrodes used in Stereo-electroencephalography (SEEG) and deep brain stimulation (DBS). This review focused on BCIs research using surface (ECoG) and depth electrodes (including SEEG, and DBS electrodes) for movement decoding on human subjects. Unlike previous reviews, the findings presented here are from the perspective of the decoding target or task. In detail, five tasks will be considered, consisting of the kinematic decoding, kinetic decoding,identification of body parts, dexterous hand decoding, and motion intention decoding. The typical studies are surveyed and analyzed. The reviewed literature demonstrated a distributed motor-related network that spanned multiple brain regions. Comparison between surface and depth studies demonstrated that richer information can be obtained using surface electrodes. With regard to the decoding algorithms, deep learning exhibited superior performance using raw signals than traditional machine learning algorithms. Despite the promising achievement made by the open-loop BCIs, closed-loop BCIs with sensory feedback are still in their early stage, and the chronic implantation of both ECoG surface and depth electrodes has not been thoroughly evaluated.

Brain computer interfaces (BCIs) have been demonstrated to have the potential to enhance motor recovery after stroke. However, some stroke patients with severe paralysis have difficulty achieving the BCI performance required for participating in BCI-based rehabilitative interventions, limiting their clinical benefits. To address this issue, we presented a BCI intervention approach that can adapt to patients' BCI performance and reported that adaptive BCI-based functional electrical stimulation (FES) treatment induced clinically significant, long-term improvements in upper extremity motor function after stroke more effectively than FES treatment without BCI intervention. These improvements were accompanied by a more optimized brain functional reorganization. Further comparative analysis revealed that stroke patients with low BCI performance (LBP) had no significant difference from patients with high BCI performance in rehabilitation efficacy improvement. Our findings suggested that the current intervention may be an effective way for LBP patients to engage in BCI-based rehabilitation treatment and may promote lasting motor recovery, thus contributing to expanding the applicability of BCI-based rehabilitation treatments to pave the way for more effective rehabilitation treatments.

Objective: A challenging task for an electroencephalography (EEG)-based asynchronous brain-computer interface (BCI) is to effectively distinguish between the idle state and the control state while maintaining a short response time and a high accuracy when commands are issued in the control state. This study proposes a novel hybrid asynchronous BCI system based on a combination of steady-state visual evoked potentials (SSVEPs) in the EEG signal and blink-related electrooculography (EOG) signals., Methods: Twelve buttons corresponding to 12 characters are included in the graphical user interface (GUI). These buttons flicker at different fixed frequencies and phases to evoke SSVEPs and are simultaneously highlighted by changing their sizes. The user can select a character by focusing on its frequency-phase stimulus and simultaneously blinking his/her eyes in accordance with its highlighting as his/her EEG and EOG signals are recorded. A multifrequency band-based canonical correlation analysis (CCA) method is applied to the EEG data to detect the evoked SSVEPs, whereas the EOG data are analyzed to identify the user's blinks. Finally, the target character is identified based on the SSVEP and blink detection results., Results: Ten healthy subjects participated in our experiments and achieved an average information transfer rate (ITR) of 105.52 bits/min, an average accuracy of 95.42%, an average response time of 1.34 s and an average false-positive rate (FPR) of 0.8%., Conclusion: The proposed BCI generates multiple commands with a high ITR and low FPR., Significance: The hybrid asynchronous BCI has great potential for practical applications in communication and control.

This paper presents a new asynchronous hybrid brain-computer interface (BCI) system that integrates a speller, a web browser, an e-mail client, and a file explorer using electroencephalographic (EEG) and electrooculography (EOG) signals. More specifically, an EOG-based button selection method, which requires the user to blink his/her eyes synchronously with the target button's flashes during button selection, is first presented. Next, we propose a mouse control method by combining EEG and EOG signals, in which the left-/right-hand motor imagery (MI)-related EEG is used to control the horizontal movement of the mouse and the blink-related EOG is used to control the vertical movement of the mouse and to select/reject a target. These two methods are further combined to develop the integrated hybrid BCI system. With the hybrid BCI, users can input text, access the internet, communicate with others via e-mail, and manage files in their computer using only EEG and EOG without any body movements. Ten healthy subjects participated in a comprehensive online experiment, and superior performance was achieved compared with our previously developed P300- and MI-based BCI and some other asynchronous BCIs, therefore demonstrating the system's effectiveness.

The key issue of electroencephalography (EEG)-based brain switches is to detect the control and idle states in an asynchronous manner. Most existing methods rely on a threshold. However, it is often time consuming to select a satisfactory threshold, and the chosen threshold might be inappropriate over a long period of time due to the variability of the EEG signals. This paper presents a new P300-based threshold-free brain switch. Specifically, one target button and three pseudo buttons, which are intensified in a random order to produce P300 potential, are set in the graphical user interface. The user can issue a switch command by focusing on the target button. Two support vector machine (SVM) classifiers, namely, SVM1 and SVM2, are used in the detection algorithm. During detection, we first obtained four SVM scores, corresponding to the four flashing buttons, by applying SVM1 to the ongoing EEG. If the SVM score corresponding to the target button was negative or not at the maximum, then an idle state was determined. Moreover, if the target button had a maximum and positive score, then we fed the four SVM scores as features into SVM2 to further discriminate the control and idle states. As an application, this brain switch was used to produce a start/stop command for an intelligent wheelchair, of which the left, right, forward, backward functions were carried out by an autonomous navigation system. Several experiments were conducted with eight healthy subjects and five patients with spinal cord injuries (SCIs). The experimental results not only demonstrated the effectiveness of our approach but also illustrated the potential application for patients with SCIs.

Objective: This study proposes an event-related potential (ERP) brain-computer interface (BCI)-based environmental control system that integrates household electrical appliances, a nursing bed, and an intelligent wheelchair to provide daily assistance to paralyzed patients with severe spinal cord injuries (SCIs). Methods: An asynchronous mode is used to switch the environmental control system on or off or to select a device (e.g., a TV) for achieving self-paced control. In the asynchronous mode, we introduce several pseudo-keys and a verification mechanism to effectively reduce the false operation rate. By contrast, when the user selects a function of the device (e.g., a TV channel), a synchronous mode is used to improve the accuracy and speed of BCI detection. Two experiments involving six SCI patients were conducted separately in a nursing bed and a wheelchair, and the patients were instructed to control the nursing bed, the wheelchair, and household electrical appliances (an electric light, an air conditioner, and a TV). Results: The average false rate of BCI commands in the control state was 10.4%, whereas the average false operation ratio was 4.9% (a false BCI command might not necessarily results in a false operation according to our system design). During the idle state, there was an average of 0.97 false positives/min, which did not result in any false operations. Conclusion: All SCI patients could use the proposed ERP BCI-based environmental control system satisfactorily. Significance: The proposed ERP-based environmental control system could be used to assist patients with severe SCIs in their daily lives. [ABSTRACT FROM AUTHOR]