Stereoelectroencephalography for continuous two-dimensional cursor control in a brain-machine interface.

Objective To investigate the application value of stereoelectroencephalography (SEEG) in the preoperative assessment of epilepsy surgery. Methods The clinical data of 78 patients with drug-refractory epilepsy assessed with SEEG before procedure at the Epilepsy Center, Guangdong 999 Brain Hospital from April 2014 to April 2015 were analyzed retrospectively. The noninvasive assessment was conducted first, and the SEEG electrode placement scheme was designed according to the clinical and imaging data, and then under the guidance of robotized stereotactic assistant (ROSA), the electrode implantation was completed. The SEEG monitoring was conducted in order to complete the resection of epileptogenic zone finally under the guidance of neuronavigation. Results Seventy-eight patients completed 81 SEEG electrode implantations (3 patients had additional electrode implantations), including 16 (20.5%) bilateral implantations. A total of 940 SEEG electrodes were implanted and the average number of implanted electrodes was 12.3 per case. Only 1 patient had an epidural hematoma in the electrode region, and other patients did not have the complications, such as serious hemorrhage, infection, cerebrospinal fluid leakage, electrode fraction or displacement. Seventy-six patients successfully completed the resection of epileptogenic zone, and 2 patients failed to undergo surgery. Fifty-nine patients were followed up for 6-18 months. The seizure control for Engel rating grades were grade Ⅰ in 47 cases (79.7%), grade Ⅱ in 2 cases (3.4%), grade Ⅲ in 6 cases (10.2%), and grade Ⅳ in 4 cases (6.8%). In 51 patients with MRI positive, the Engel grade Ⅰ was in 42 cases (82.4%); in 8 patients with MRI negative, the Engel grade Ⅰ was in 5 cases. There was no significant difference in the seizure control rate between the 2 groups (P>0.05). Conclusion The surgical treatment of epilepsy under the guidance of SEEG is both safe and effective. Key words: Epilepsy; Electroencephalography; Stereoelectroencephalography; Robotized stereotactic assistant; Multimodel images

ObjectivesOne third of patients with drug resistant focal mesial temporal lobe epilepsy (MTLE) fail to achieve long-term seizure freedom following temporal lobe resections. Reasons for failure may include ictal onset...

To clarify the exact anatomic relationship of electrically identified hand areas to the central sulcus, we constructed cortical surface renderings of magnetic resonance images (MRI) to locate the central sulcus accurately and measured the distances of stimulated points from the central sulcus and the Sylvian fissure. We obtained hand responses in 33 patients who underwent implantation of subdural grid electrodes for evaluation and surgical treatment of intractable epilepsy and analyzed these responses according to the presence of motor, sensory, mixed motor and sensory, and arrest responses. Hand motor responses occurred not only in the precentral gyrus but also in the postcentral gyrus, with great variability in superior-to-inferior distribution. Sensory responses also occurred in both the precentral and postcentral gyri with a distribution more ventral than that of motor responses. Mixed motor and sensory responses tended to be limited to the middle part of the central sulcus. Sites where electrical stimulation arrested simple hand repetitive voluntary movements occurred widely throughout the premotor and primary sensorimotor cortices. These data indicate a marked variability in the location of the human cortical hand area, and suggest that motor and sensory hand cortices overlap and are not divided in a simple manner by the central sulcus.

It is impossible for severely disabled people to browse or learn through the Internet due to the mere lack of independent control of the mouse. This paper proposes a brain computer interface (BCI) to aid severely disabled individuals, such as people disabled by amyotrophic lateral sclerosis (ALS), in browsing or learning on the Internet. By analyzing specific components of event-related potentials (ERPs), cursor control can be achieved. The cursor can be controlled by brain waves in a user-friendly manner to move or click on the web page. The major contributions of this research include: (1) designing a BCI for disabled people, (2) embedding the BCI's cursor controller into a web page, (3) conducting clinical experiments, (4) analysing the experimental results, system accuracy, and effectiveness, and (5) evaluating the system's practicability and giving suggestions for future work. There are two innovative technologies proposed in this research: (1) a specific component of ERPs located at O1, the N2P3 (the difference between the peak and trough of N200 and P300), was used to differentiate targeted from non-targeted (non-selected) signals, (2) instead of a fixed position style BCI, a movable watermark style BCI which follows the cursor was designed. The novel technique supported by a user-friendly interface helps the disabled have contact with the outside world resulting in positive emotions.

Comparison of postoperative recovery indicators between stereoelectroencephalography and subdural grid electrode implantation for drug-resistant epilepsy.

Neuromodulatory Role of Revascularization Surgery in Moyamoya Disease

Surgical treatment of cortical dysplasia (CD) together with intractable seizures is challenging because both visualization and localization of the lesion are difficult, correlation with seizure foci requires comprehensive study, and the surgical outcomes reported thus far are unsatisfactory. The authors report their experience in the surgical treatment of CD classified according to a surgical point of view. The definition of CD used in this study was a dysplastic lesion visible on magnetic resonance (MR) images or a lesion that, although not visible on MR images, was diagnosed as moderate-to-severe dysplasia by using pathological analysis. During the last 4.5 years, the authors treated 36 patients with intractable epilepsy accompanied by CD. They divided the 36 cases of CD into four characteristic groups: Group A, diffuse bilateral hemispheric dysplasia; Group B, diffuse lobar dysplasia; Group C, focal dysplasia; and Group D, a moderate to severe degree of CD with a normal appearance on MR images. All but one patient in Group C were monitored in the epilepsy monitoring unit by using subdural electrodes for seizure localization and functional mapping. The incidence of CD among a cohort of 291 patients who had undergone epilepsy surgery at the authors' center during the study period was 12.4%. The mean age of the 36 patients was 21.3 years and the mean age at seizure onset was 8.5 years. The mean follow-up period was 26 months. Twenty-six patients (72.2%) belonged to Engel Class I or II (20 and six, respectively). There were five cases in Group A, nine in Group B, nine in Group C, and 13 in Group D. Patients in Groups A and B were significantly younger at seizure onset and had significantly poorer surgical outcomes compared with patients in Groups C and D (p < 0.05). If outcome is compared on the basis of the extent of removal of CD, patients in whom CD was completely removed had significantly better outcomes than those in whom CD was only partially removed (p < 0.001). The authors conclude that intractable epilepsy accompanied by CD can be treated surgically using comprehensive preoperative approaches. Deliberate resective procedures aimed at complete removal of dysplastic tissue ensure excellent seizure control without permanent neurological deficit.

Stereoelectroencephalography (SEEG) is an established invasive diagnostic technique for use in patients with drug-resistant focal epilepsy evaluated before resective epilepsy surgery. The factors that influence the accuracy of electrode implantation are not fully understood. Adequate accuracy prevents the risk of major surgery complications. Precise knowledge of the anatomical positions of individual electrode contacts is crucial for the interpretation of SEEG recordings and subsequent surgery. We developed an image processing pipeline to localize implanted electrodes and detect individual contact positions using computed tomography (CT), as a substitute for time-consuming manual labeling. The algorithm automates measurement of parameters of the electrodes implanted in the skull (bone thickness, implantation angle and depth) for use in modeling of predictive factors that influence implantation accuracy. Fifty-four patients evaluated by SEEG were analyzed. A total of 662 SEEG electrodes with 8,745 contacts were stereotactically inserted. The automated detector localized all contacts with better accuracy than manual labeling (p < 0.001). The retrospective implantation accuracy of the target point was 2.4 ± 1.1 mm. A multifactorial analysis determined that almost 58% of the total error was attributable to measurable factors. The remaining 42% was attributable to random error. SEEG contacts can be reliably marked by our proposed method. The trajectory of electrodes can be parametrically analyzed to predict and validate implantation accuracy using a multifactorial model. This novel, automated image processing technique is a potentially clinically important, assistive tool for increasing the yield, efficiency, and safety of SEEG.

Stereoelectroencephalography (SEEG) is widely performed on individuals with medically refractory epilepsy for whom invasive seizure localization is desired. Despite increasing adoption in many centers across the world, no standardized electrode naming convention exists, generating confusion among both clinical and research teams. We have developed a novel nomenclature, named the Standardized Electrode Nomenclature for SEEG Applications system. Concise, unique, informative, and unambiguous labels provide information about entry point, deep targets, and relationships between electrodes. Inter-rater agreement was evaluated by comparing original electrode names from 10 randomly sampled cases (including 136 electrodes) with those prospectively assigned by four additional blinded raters. The Standardized Electrode Nomenclature for SEEG Application system was prospectively implemented in 40 consecutive patients undergoing SEEG monitoring at our institution, creating unique electrode names in all cases, and facilitating implantation design, SEEG recording and mapping interpretation, and treatment planning among neurosurgeons, neurologists, and neurophysiologists. The inter-rater percent agreement for electrode names among two neurosurgeons, two epilepsy neurologists, and one neurosurgical fellow was 97.5%. This standardized naming convention, Standardized Electrode Nomenclature for SEEG Application, provides a simple, concise, reproducible, and informative method for specifying the target(s) and relative position of each SEEG electrode in each patient, allowing for successful sharing of information in both the clinical and research settings. General adoption of this nomenclature could pave the way for improved communication and collaboration between institutions.

Stereoelectroencephalography (SEEG) is used to identify the epileptogenic zone (EZ) in patients with epilepsy for potential surgical intervention. Occasionally, the EZ is difficult to localize even after an SEEG implantation. To demonstrate a safe technique for placing additional electrodes in ongoing SEEG evaluations. Describe efficacy, complications, and surgical outcomes. An operative technique which involves maintaining previously placed electrodes and sterilely placing new electrodes was developed and implemented. All patients who underwent placement of additional SEEG electrodes during the same admission were retrospectively reviewed. A total of 14 patients met criteria and had undergone SEEG evaluation with 198 electrodes implanted. A total 93% of patients (13/14) had nonlesional epilepsy. After unsuccessful localization of the EZ after a mean of 9.6 d of monitoring, each patient underwent additional placement of electrodes (5.5 average electrodes per patient) to augment the original implantation. At no point did any patients develop new hemorrhage, infection, wound breakdown, or require any kind of additional antimicrobial treatment. A total 64% (9/14) of patients were able to undergo surgery aimed at removing the EZ guided by the additional SEEG electrodes. A total 44% (4/9) of surgical patients had Engel class I outcomes at an average follow-up time of 11 mo. Placing additional SEEG electrodes, while maintaining the previously placed electrodes, appears to be safe, effective, and had no infectious complications. When confronted with difficult-to-localize epilepsy even after invasive monitoring, it appears to be safe and potentially clinically effective to place additional electrodes during the same admission.

Brain-computer interfaces (BCIs) record brain activity and translate the information into useful control signals. They can be used to restore function to people with paralysis by controlling end effectors such as computer cursors and robotic limbs. Communication neural prostheses are BCIs that control user interfaces on computers or mobile devices. Here we demonstrate a communication prosthesis by simulating a typing task with two rhesus macaques implanted with electrode arrays. The monkeys used two of the highest known performing BCI decoders to type out words and sentences when prompted one symbol/letter at a time. On average, Monkeys J and L achieved typing rates of 10.0 and 7.2 words per minute (wpm), respectively, copying text from a newspaper article using a velocity-only two dimensional BCI decoder with dwell-based symbol selection. With a BCI decoder that also featured a discrete click for key selection, typing rates increased to 12.0 and 7.8 wpm. These represent the highest known achieved communication rates using a BCI. We then quantified the relationship between bitrate and typing rate and found it approximately linear: typing rate in wpm is nearly three times bitrate in bits per second. We also compared the metrics of achieved bitrate and information transfer rate and discuss their applicability to real-world typing scenarios. Although this study cannot model the impact of cognitive load of word and sentence planning, the findings here demonstrate the feasibility of BCIs to serve as communication interfaces and represent an upper bound on the expected achieved typing rate for a given BCI throughput.

In the near future, brain-computer interface (BCI) applications for non-disabled users will require multimodal interaction and tolerance to dynamic environment. However, this conflicts with the highly sensitive recording techniques used for BCIs, such as electroencephalography (EEG). Advanced machine learning and signal processing techniques are required to decorrelate desired brain signals from the rest. This paper proposes a signal processing pipeline and two classification methods suitable for multiclass EEG analysis. The methods were tested in an experiment on separating left/right hand imagery in presence/absence of speech. The analyses showed that the presence of speech during motor imagery did not affect the classification accuracy significantly and regardless of the presence of speech, the proposed methods were able to separate left and right hand imagery with an accuracy of 60%. The best overall accuracy achieved for the 5-class separation of all the tasks was 47% and both proposed methods performed equally well. In addition, the analysis of event-related spectral power changes revealed characteristics related to motor imagery and speech.

ObjectiveVarious forms of vascular imaging are performed to identify vessels that should be avoided during stereoelectroencephalography (SEEG) planning. Digital subtraction angiography (DSA) is the gold standard for intracranial vascular imaging. DSA is an invasive investigation, and a balance is necessary to identify all clinically relevant vessels and not to visualize irrelevant vessels that may unnecessarily restrict electrode placement. We sought to estimate the size of vessels that are clinically significant for SEEG planning.MethodsThirty‐three consecutive patients who underwent 354 SEEG electrode implantations planned with computer‐assisted planning and DSA segmentation between 2016 and 2018 were identified from a prospectively maintained database. Intracranial positions of electrodes were segmented from postimplantation computed tomography scans. Each electrode was manually reviewed using “probe‐eye view” with the raw preoperative DSA images for vascular conflicts. The diameter of vessels and the location of conflicts were noted. Vessel conflicts identified on raw DSA images were cross‐referenced against other modalities to determine whether the conflict could have been detected.ResultsOne hundred sixty‐six vessel conflicts were identified between electrodes and DSA‐identified vessels, with 0‐3 conflicts per electrode and a median of four conflicts per patient. The median diameter of conflicting vessels was 1.3 mm (interquartile range [IQR] = 1.0‐1.5 mm). The median depth of conflict was 31.0 mm (IQR = 14.3‐45.0 mm) from the cortical surface. The addition of sulcal models to DSA, magnetic resonance venography (MRV), and T1 + gadolinium images, as an exclusion zone during computer‐assisted planning, would have prevented the majority of vessel conflicts. We were unable to determine whether vessels were displaced or transected by the electrodes.SignificanceVascular segmentation from DSA images was significantly more sensitive than T1 + gadolinium or MRV images. Electrode conflicts with vessels 1‐1.5 mm in size did not result in a radiologically detectable or clinically significant hemorrhage and could potentially be excluded from consideration during SEEG planning.

Objective. Brain–computer interfaces (BCIs) translate neural activity into control signals for assistive devices in order to help people with motor disabilities communicate effectively. In this work, we introduce a new BCI architecture that improves control of a BCI computer cursor to type on a virtual keyboard. Approach. Our BCI architecture incorporates an external artificial intelligence (AI) that beneficially augments the movement trajectories of the BCI. This AI-BCI leverages past user actions, at both long (100 s of seconds ago) and short (100 s of milliseconds ago) timescales, to modify the BCI’s trajectories. Main results. We tested our AI-BCI in a closed-loop BCI simulator with nine human subjects performing a typing task. We demonstrate that our AI-BCI achieves: (1) categorically higher information communication rates, (2) quicker ballistic movements between targets, (3) improved precision control to ‘dial in’ on targets, and (4) more efficient movement trajectories. We further show that our AI-BCI increases performance across a wide control quality spectrum from poor to proficient control. Significance. This AI-BCI architecture, by increasing BCI performance across all key metrics evaluated, may increase the clinical viability of BCI systems.

Cost-effectiveness analysis of invasive EEG monitoring in drug-resistant epilepsy